- TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Learn how to use TensorFlow 2.0 in this full tutorial course for beginners. This course is designed for Python programmers looking to enhance their knowledge and skills in machine learning and artificial intelligence.

Throughout the 8 modules in this course you will learn about fundamental conc...
Learn how to use TensorFlow 2.0 in this full tutorial course for beginners. This course is designed for Python programmers looking to enhance their knowledge and skills in machine learning and artificial intelligence.

Throughout the 8 modules in this course you will learn about fundamental concepts and methods in ML & AI like core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning.

Each of these modules include in-depth explanations and a variety of different coding examples. After completing this course you will have a thorough knowledge of the core techniques in machine learning and AI and have the skills necessary to apply these techniques to your own data-sets and unique problems.


⭐️ Google Colaboratory Notebooks ⭐️

📕 Module 2: Introduction to TensorFlow - #forceEdit=true&sandboxMode=true" rel="nofollow noopener noreferrer" target="_blank">https://colab.research.google.com/drive/1F_EWVKa8rbMXi3_fG0w7AtcscFq7Hi7B#forceEdit=true&sandboxMode=true
📗 Module 3: Core Learning Algorithms - #forceEdit=true&sandboxMode=true" rel="nofollow noopener noreferrer" target="_blank">https://colab.research.google.com/drive/15Cyy2H7nT40sGR7TBN5wBvgTd57mVKay#forceEdit=true&sandboxMode=true
📘 Module 4: Neural Networks with TensorFlow - #forceEdit=true&sandboxMode=true" rel="nofollow noopener noreferrer" target="_blank">https://colab.research.google.com/drive/1m2cg3D1x3j5vrFc-Cu0gMvc48gWyCOuG#forceEdit=true&sandboxMode=true
📙 Module 5: Deep Computer Vision - #forceEdit=true&sandboxMode=true" rel="nofollow noopener noreferrer" target="_blank">https://colab.research.google.com/drive/1ZZXnCjFEOkp_KdNcNabd14yok0BAIuwS#forceEdit=true&sandboxMode=true
📔 Module 6: Natural Language Processing with RNNs - #forceEdit=true&sandboxMode=true" rel="nofollow noopener noreferrer" target="_blank">https://colab.research.google.com/drive/1ysEKrw_LE2jMndo1snrZUh5w87LQsCxk#forceEdit=true&sandboxMode=true
📒 Module 7: Reinforcement Learning - #forceEdit=true&sandboxMode=true" rel="nofollow noopener noreferrer" target="_blank">https://colab.research.google.com/drive/1IlrlS3bB8t1Gd5Pogol4MIwUxlAjhWOQ#forceEdit=true&sandboxMode=true


⭐️ Course Contents ⭐️

⌨️ (00:03:25) Module 1: Machine Learning Fundamentals
⌨️ (00:30:08) Module 2: Introduction to TensorFlow
⌨️ (01:00:00) Module 3: Core Learning Algorithms
⌨️ (02:45:39) Module 4: Neural Networks with TensorFlow
⌨️ (03:43:10) Module 5: Deep Computer Vision - Convolutional Neural Networks
⌨️ (04:40:44) Module 6: Natural Language Processing with RNNs
⌨️ (06:08:00) Module 7: Reinforcement Learning with Q-Learning
⌨️ (06:48:24) Module 8: Conclusion and Next Steps


⭐️ About the Author ⭐️

The author of this course is Tim Ruscica, otherwise known as “Tech With Tim” from his educational programming YouTube channel. Tim has a passion for teaching and loves to teach about the world of machine learning and artificial intelligence. Learn more about Tim from the links below:
🔗 YouTube: https://www.youtube.com/channel/UC4JX40jDee_tINbkjycV4Sg
🔗 LinkedIn: https://www.linkedin.com/in/tim-ruscica/

--

Learn to code for free and get a developer job: https://www.freecodecamp.org

Read hundreds of articles on programming: https://freecodecamp.org/news
Intro - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Intro

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:00:00 - 00:03:24
Hi! I'm  hours in, I am learning to work with tf in University, so I already knew most of the things he showed until then. I am specifically looking for an explanation of tensorflow ops and how to create custom ones. Is this covered in this class at some point? Or do you know where I can find a tutorial on this (other than tf's documentation)? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Hi! I'm hours in, I am learning to work with tf in University, so I already knew most of the things he showed until then. I am specifically looking for an explanation of tensorflow ops and how to create custom ones. Is this covered in this class at some point? Or do you know where I can find a tutorial on this (other than tf's documentation)?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:02:45 - 06:52:08
ML Fundamentals - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

ML Fundamentals

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:03:24 - 00:30:29
: Machine Learning Fundamentals () - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

: Machine Learning Fundamentals ()

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:03:25 - 00:30:08
: Machine Learning Fundamentals (​) - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

: Machine Learning Fundamentals (​)

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:03:25 - 00:30:08
⌨️ () Module 1: Machine Learning Fundamentals - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

⌨️ () Module 1: Machine Learning Fundamentals

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:03:25 - 00:30:08
~Introduction. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

~Introduction.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:03:30 - 06:52:08
“I am not artistic whatsoever” - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

“I am not artistic whatsoever”

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:04:00 - 00:27:00
tf.keras.layers.LSTM(32) is not equal to the size of the embedding word ! It is the size of output of LSTM.You could have tf.keras.layers.LSTM(64) for example - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

tf.keras.layers.LSTM(32) is not equal to the size of the embedding word ! It is the size of output of LSTM.You could have tf.keras.layers.LSTM(64) for example

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:05:17 - 06:52:08
At  you said labels are the output information we get by predicting from features and at 18:36 you say that features and labels are combined to get the output we need. Its pretty confusing. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

At you said labels are the output information we get by predicting from features and at 18:36 you say that features and labels are combined to get the output we need. Its pretty confusing.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:17:23 - 06:52:08
.import tensorflow as tfprint(tf.__version__) - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

.import tensorflow as tfprint(tf.__version__)

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:18:03 - 06:52:08
Isn't the diagram at  an example of Clustering?I always thought the output for Unsupervised Learning was not present and the model learns from trial and error pattern.I don't see a direct link between unsupervised learning and Clustering.Please can someone clear my doubts? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Isn't the diagram at an example of Clustering?I always thought the output for Unsupervised Learning was not present and the model learns from trial and error pattern.I don't see a direct link between unsupervised learning and Clustering.Please can someone clear my doubts?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:24:15 - 06:52:08
@ And that is how they build the Matrix. Great video. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

@ And that is how they build the Matrix. Great video.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:26:23 - 06:52:08
Does enviorment equals to state? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Does enviorment equals to state?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:26:57 - 06:52:08
taking a leak - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

taking a leak

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:27:00 - 00:27:49
humping a pole - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

humping a pole

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:27:49 - 06:52:08
the agent at  is a pole dancer.  Really enjoying the content brother! Great job.  You have already destroyed any college professor I have experienced. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

the agent at is a pole dancer. Really enjoying the content brother! Great job. You have already destroyed any college professor I have experienced.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:28:13 - 06:52:08
He just missed the chance to end the introduction perfectly at - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

He just missed the chance to end the introduction perfectly at

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:30:00 - 06:52:08
: Introduction to TensorFlow () - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

: Introduction to TensorFlow ()

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:30:08 - 01:00:00
: Introduction to TensorFlow (​) - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

: Introduction to TensorFlow (​)

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:30:08 - 01:00:00
⌨️ () Module 2: Introduction to TensorFlow - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

⌨️ () Module 2: Introduction to TensorFlow

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:30:08 - 01:00:00
Intro to Tensorflow - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Intro to Tensorflow

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:30:29 - 06:52:08
. TensorFlow has two main components: graph and session - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

. TensorFlow has two main components: graph and session

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:33:05 - 05:56:31
I didn't understand that. can someone please try to explain it to me? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

I didn't understand that. can someone please try to explain it to me?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:33:12 - 06:52:08
:Failed  Reason: No Water/late    Time in: - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

:Failed Reason: No Water/late Time in:

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:38:04 - 06:52:08
Physics: a tensor is something that transforms like a tensor - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Physics: a tensor is something that transforms like a tensor

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:43:28 - 06:52:08
don't mind me, just reminding myself - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

don't mind me, just reminding myself

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:46:55 - 06:52:08
at   you have to explicitly mention, dtype=tf.int16, else it doesn't consider the argumentINPUT - tensor_int2 = tf.Variable(200, dtype=tf.int64)OUTPUT - <tf.Variable 'Variable:0' shape=() dtype=int64, numpy=200> andINPUT  - tensor_int3 = tf.Variable(300, tf.int16),OUTPUT - <tf.Variable 'Variable:0' shape=() dtype=int32, numpy=300> - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

at you have to explicitly mention, dtype=tf.int16, else it doesn't consider the argumentINPUT - tensor_int2 = tf.Variable(200, dtype=tf.int64)OUTPUT - <tf.Variable 'Variable:0' shape=() dtype=int64, numpy=200> andINPUT - tensor_int3 = tf.Variable(300, tf.int16),OUTPUT - <tf.Variable 'Variable:0' shape=() dtype=int32, numpy=300>

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:46:57 - 06:52:08
rank - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

rank

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:48:00 - 00:50:00
why does it say dtype = int32 in <tf.Tensor: shape=(), dtype=int32, numpy=2>,even thou he set it to tf.string ?can anyone explain pleas. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

why does it say dtype = int32 in <tf.Tensor: shape=(), dtype=int32, numpy=2>,even thou he set it to tf.string ?can anyone explain pleas.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:49:36 - 06:52:08
why is the dtype int32 despite the variable being a string type? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

why is the dtype int32 despite the variable being a string type?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:49:46 - 06:52:08
shape - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

shape

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:50:00 - 00:52:00
@ the shape explanation is wrong. You have a 2 by 2 shape for the rank2 variable because you have 2 elements in 2 lists/arrays, not because you have 2 elements in each of the lists (i.e. you could have 2 elements in 3 lists and have a 3 by 2 shape). - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

@ the shape explanation is wrong. You have a 2 by 2 shape for the rank2 variable because you have 2 elements in 2 lists/arrays, not because you have 2 elements in each of the lists (i.e. you could have 2 elements in 3 lists and have a 3 by 2 shape).

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:50:35 - 06:52:08
At  the way you suggested to get a rank three tensor gave an error --rank2_tensor = tf.Variable([["test", "ok", ["ok","oj"]]],["test", "yes", ["zyx", "oj"]]], tf.string) - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

At the way you suggested to get a rank three tensor gave an error --rank2_tensor = tf.Variable([["test", "ok", ["ok","oj"]]],["test", "yes", ["zyx", "oj"]]], tf.string)

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:51:47 - 06:52:08
change in shape - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

change in shape

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:52:00 - 00:55:10
manual save - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

manual save

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:52:18 - 06:52:08
thing at  , can anyone help me out!😭 - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

thing at , can anyone help me out!😭

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:54:25 - 06:52:08
types of tensors - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

types of tensors

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:55:10 - 00:56:30
evaluating Tensors - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

evaluating Tensors

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:56:30 - 00:57:25
For running seesions  at :with tf.compat.v1.Session() as sess:print(tensor0.eval()) - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

For running seesions at :with tf.compat.v1.Session() as sess:print(tensor0.eval())

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:57:03 - 06:52:08
sources - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

sources

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:57:25 - 00:57:40
practice - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

practice

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:57:40 - 01:00:00
we have 125 of 5 vectors not 5 vectors of 125 - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

we have 125 of 5 vectors not 5 vectors of 125

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
00:59:20 - 06:52:08
: Core Learning Algorithms () - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

: Core Learning Algorithms ()

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:00:00 - 02:45:39
: Core Learning Algorithms (​) - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

: Core Learning Algorithms (​)

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:00:00 - 02:45:39
Hey just to confirm, are you sure the  is the linear regression and not linear classification. i am not able to get this. we are classifying whether it will be survived or not. based on the input data. can some one please help with this - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Hey just to confirm, are you sure the is the linear regression and not linear classification. i am not able to get this. we are classifying whether it will be survived or not. based on the input data. can some one please help with this

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:00:00 - 06:52:08
Linear Regression  () - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Linear Regression ()

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:00:00 - 01:54:00
This end-to-end walkthrough trains a logistic regression model (binary classification and not a regression) using the tf.estimator API. The model is often used as a baseline for other, more complex, algorithms.I guess it is not a regression problem but it is the classification problem - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

This end-to-end walkthrough trains a logistic regression model (binary classification and not a regression) using the tf.estimator API. The model is often used as a baseline for other, more complex, algorithms.I guess it is not a regression problem but it is the classification problem

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:00:00 - 06:52:08
tensorflow core learning algorithms - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

tensorflow core learning algorithms

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:00:00 - 01:02:40
⌨️ () Module 3: Core Learning Algorithms - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

⌨️ () Module 3: Core Learning Algorithms

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:00:00 - 02:45:39
= tensor[]  # selects second and fourth rowprint(row2and4) - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

= tensor[] # selects second and fourth rowprint(row2and4)

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:00:02 - 06:52:08
Great Video (even if i havent finished it yet)! The example in  is NOT regression is Classification. You have 2 classes (survived or not) and you try to classify the passengers. In other words, the result would always be a probability, you cannot use the same methodology to predict the age, for example . - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Great Video (even if i havent finished it yet)! The example in is NOT regression is Classification. You have 2 classes (survived or not) and you try to classify the passengers. In other words, the result would always be a probability, you cannot use the same methodology to predict the age, for example .

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:00:10 - 06:52:08
linear regression - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

linear regression

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:02:40 - 01:13:00
"Do not Memorize just Understand" - made my mind to stay "calm". Felt to thank at that time frame... "Thank You!" - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

"Do not Memorize just Understand" - made my mind to stay "calm". Felt to thank at that time frame... "Thank You!"

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:02:44 - 06:52:08
thanks for the explanation at ! - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

thanks for the explanation at !

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:06:06 - 06:52:08
at  he fitted best fit line not the plane in 3d graph in linear regression algorithm RIP mathematics.😥😥 - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

at he fitted best fit line not the plane in 3d graph in linear regression algorithm RIP mathematics.😥😥

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:10:59 - 06:52:08
() Wouldn't linear regression in 3D give you a plane rather than a line? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

() Wouldn't linear regression in 3D give you a plane rather than a line?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:11:00 - 06:52:08
Around  the "line of best fit" with two predictor variables should be a plane, no? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Around the "line of best fit" with two predictor variables should be a plane, no?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:11:11 - 06:52:08
D you're supposed to fit the best plane, not the line. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

D you're supposed to fit the best plane, not the line.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:11:13 - 06:52:08
BIG correction: In 3 dimensions we are trying to fit a 2d plane through the points NOT a single line. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

BIG correction: In 3 dimensions we are trying to fit a 2d plane through the points NOT a single line.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:12:00 - 06:52:08
Bookmark - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Bookmark

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:12:50 - 06:52:08
How is it possible to get out the parameters for the equation, after training in the case of the Titanic dataset (see from )? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

How is it possible to get out the parameters for the equation, after training in the case of the Titanic dataset (see from )?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:13:00 - 06:52:08
setup and import - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

setup and import

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:13:00 - 01:15:40
@ you did not mention what the 'from six.moves import urllib stands for.  Was curious what that was? It's possible you accidentally skipped over this one.  You can always add an annotation on the video if you know the answer so it shows for others. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

@ you did not mention what the 'from six.moves import urllib stands for. Was curious what that was? It's possible you accidentally skipped over this one. You can always add an annotation on the video if you know the answer so it shows for others.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:15:00 - 06:52:08
data - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

data

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:15:40 - 06:52:08
the fare around  referred to the amount of money they paid to travel on the boat. A fare is a payment made for a trip. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

the fare around referred to the amount of money they paid to travel on the boat. A fare is a payment made for a trip.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:19:00 - 06:52:08
Parch stands for number of parents and children aboardFare was obviously the price passengers had to pay for the journey - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Parch stands for number of parents and children aboardFare was obviously the price passengers had to pay for the journey

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:19:31 - 06:52:08
in  you say that  "Name" says wether a person has survived or not. This doesn't seem to be true. When you do "dftrain.loc[2]" "Name" is 2. Also it can't be true since we deleted the column from our dataframe. "Name" just tells you the index of the row. In this example the first one. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

in you say that "Name" says wether a person has survived or not. This doesn't seem to be true. When you do "dftrain.loc[2]" "Name" is 2. Also it can't be true since we deleted the column from our dataframe. "Name" just tells you the index of the row. In this example the first one.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:25:30 - 06:52:08
At  print(dftrain.loc[0], y_train.loc[0]) actually only prints the value of dftrain.loc[0] and Name:0 just refers to the index of the entry. If you want to display y_train.loc[0] you have to print it seperately with print(y_train.loc[0]) or print(y_train[0]) (both work) - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

At print(dftrain.loc[0], y_train.loc[0]) actually only prints the value of dftrain.loc[0] and Name:0 just refers to the index of the entry. If you want to display y_train.loc[0] you have to print it seperately with print(y_train.loc[0]) or print(y_train[0]) (both work)

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:25:38 - 06:52:08
I think that is not the "encoded value" rather the real unique number of sibling, same with the "parch". Now I am wondering is TF feature column really encode categorical features, or just making a literal feature column from the vocab? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

I think that is not the "encoded value" rather the real unique number of sibling, same with the "parch". Now I am wondering is TF feature column really encode categorical features, or just making a literal feature column from the vocab?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:36:34 - 06:52:08
Well I suppose they've to add this function as an external command in tensorflow v.3.0 would be greatly helpful - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Well I suppose they've to add this function as an external command in tensorflow v.3.0 would be greatly helpful

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:41:07 - 06:52:08
why does it need to be a function inside a function, though? Can't this be reduced to a single function? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

why does it need to be a function inside a function, though? Can't this be reduced to a single function?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:43:30 - 06:52:08
I was actually hoping to hear an explanation for the need of an input function and not just a remark to read the documentation - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

I was actually hoping to hear an explanation for the need of an input function and not just a remark to read the documentation

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:43:32 - 06:52:08
I don't really see the use of make_input_fn.  You could just use one input_function with different arguments for test and train. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

I don't really see the use of make_input_fn. You could just use one input_function with different arguments for test and train.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:43:39 - 06:52:08
Do I need to write the same line of code as in   for my every Machine Learning problem? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Do I need to write the same line of code as in for my every Machine Learning problem?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:43:58 - 06:52:08
"We're  not trining it... we're just training it"  ;D - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

"We're not trining it... we're just training it" ;D

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:44:11 - 06:52:08
creating a model with titanic dataset - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

creating a model with titanic dataset

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:44:30 - 06:52:08
Can someone clear my confusion at  he use linearClassifier and the dataset he used have a categorical label which shows it is a classification problem up till now ok, however at the moment he is discussing regression so he should use linearRegressor?  this is more confusing at  2:02:16 when he discusses the difference between classification and regression. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Can someone clear my confusion at he use linearClassifier and the dataset he used have a categorical label which shows it is a classification problem up till now ok, however at the moment he is discussing regression so he should use linearRegressor? this is more confusing at 2:02:16 when he discusses the difference between classification and regression.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:44:35 - 06:52:08
of this course (core learning) but there is something not clear in my mind,  why a .LinearClassifier is used? is it for linear regression algorithms or a for linear classifier ones? the goal is to find a line that fits the dataset or a line that "splits" it? pls halp - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

of this course (core learning) but there is something not clear in my mind, why a .LinearClassifier is used? is it for linear regression algorithms or a for linear classifier ones? the goal is to find a line that fits the dataset or a line that "splits" it? pls halp

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:45:00 - 06:52:08
Edit: Me literally dancing just because I got better accuracy at  XD - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Edit: Me literally dancing just because I got better accuracy at XD

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:46:47 - 06:52:08
Congratulations to those who reached this part. You've just created a machine learning program. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Congratulations to those who reached this part. You've just created a machine learning program.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:47:06 - 06:52:08
Thank you for the time and effort. Helps me a lot. But,  You talk about probabilities of "survived" and "did not survive". Two classes, but the sample says this is regression. Isn't it a classification problem?  Shouldn't a regression be predicting a number which is the chance of survival as a single number? Thank you. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Thank you for the time and effort. Helps me a lot. But, You talk about probabilities of "survived" and "did not survive". Two classes, but the sample says this is regression. Isn't it a classification problem? Shouldn't a regression be predicting a number which is the chance of survival as a single number? Thank you.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:51:39 - 06:52:08
Checkpoint - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Checkpoint

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:54:00 - 06:52:08
Classification () - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Classification ()

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
01:54:00 - 02:17:07
is there any particular reason why we didn't just create an inner function previously or is using lamba just clearer coding? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

is there any particular reason why we didn't just create an inner function previously or is using lamba just clearer coding?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
02:06:54 - 06:52:08
You say 0.39 is pretty bad:me looks at my loss: *sees 0.948* - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

You say 0.39 is pretty bad:me looks at my loss: *sees 0.948*

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
02:08:44 - 06:52:08
around - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

around

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
02:11:05 - 06:52:08
Around  isdigit() will not really work. In you case it worked because you didn't enter an alphanumeric value as input. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Around  isdigit() will not really work. In you case it worked because you didn't enter an alphanumeric value as input.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
02:14:00 - 06:52:08
The code you use at the end of the classifcation model around  to insert the values and get the prediction, is it possible to do that same code for a dnnregressor? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

The code you use at the end of the classifcation model around to insert the values and get the prediction, is it possible to do that same code for a dnnregressor?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
02:14:14 - 06:52:08
K-Means Clustering () - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

K-Means Clustering ()

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
02:17:07 - 02:24:56
Hidden Markov Models () - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Hidden Markov Models ()

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
02:24:56 - 06:52:08
Ignore this just saving my prog - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Ignore this just saving my prog

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
02:35:34 - 06:52:08
at , it's accidentally backwards in the ipynb file. "initial_distribution = tfd.Categorical(probs=[0.2, 0.8])" should be [0.8, 0.2] like in the video. Actually, all the numbers in that code block are mismatched from the video and don't match the weather diagram. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

at , it's accidentally backwards in the ipynb file. "initial_distribution = tfd.Categorical(probs=[0.2, 0.8])" should be [0.8, 0.2] like in the video. Actually, all the numbers in that code block are mismatched from the video and don't match the weather diagram.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
02:36:20 - 06:52:08
: Neural Networks with TensorFlow () - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

: Neural Networks with TensorFlow ()

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
02:45:39 - 03:43:10
: Neural Networks with TensorFlow (​) - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

: Neural Networks with TensorFlow (​)

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
02:45:39 - 03:43:10
⌨️ () Module 4: Neural Networks with TensorFlow - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

⌨️ () Module 4: Neural Networks with TensorFlow

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
02:45:39 - 03:43:10
NN starts here. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

NN starts here.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
02:46:35 - 06:52:08
I have a question at  if the value of a neuron in the next layer is calculated by Ewi xi + b. That means all the neurons in the next layer will have the same value because the same calculation will be carried out since wi ni and b will be gotten from the previous layer for each neuron. Can someone please explain this I think I have the wrong ideas - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

I have a question at if the value of a neuron in the next layer is calculated by Ewi xi + b. That means all the neurons in the next layer will have the same value because the same calculation will be carried out since wi ni and b will be gotten from the previous layer for each neuron. Can someone please explain this I think I have the wrong ideas

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:02:47 - 06:52:08
I like your example of sigmod which provides more dimensions to the network capability at , which will lead to better predictions. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

I like your example of sigmod which provides more dimensions to the network capability at , which will lead to better predictions.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:08:10 - 06:52:08
.If someone is confused how we can get better features at higher dimensions. I understood it in this way.Think that there is bad point in 2 dimensions (like in a square or a circle ) , now when we shift the same bad point to 3 dimensions or 4 dimensions .They take the shape of something like a cube or sphere which increases the size of the bad point. Similarly , when extracting features at higher dimensions can yield to better features. I hope it helps :) - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

.If someone is confused how we can get better features at higher dimensions. I understood it in this way.Think that there is bad point in 2 dimensions (like in a square or a circle ) , now when we shift the same bad point to 3 dimensions or 4 dimensions .They take the shape of something like a cube or sphere which increases the size of the bad point. Similarly , when extracting features at higher dimensions can yield to better features. I hope it helps :)

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:09:00 - 06:52:08
i dont' thing cost and loss are the same thing. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

i dont' thing cost and loss are the same thing.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:12:33 - 06:52:08
You can display the image in greyscale like this: `plt.imshow(train_images[0], cmap="gray")` - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

You can display the image in greyscale like this: `plt.imshow(train_images[0], cmap="gray")`

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:23:50 - 06:52:08
On  you train the neural network with 10 and than 8 and than 1 epoch but its actually still the same model you train, thats why the loss on start is quite low already. So basically you did a epochs=19 run - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

On you train the neural network with 10 and than 8 and than 1 epoch but its actually still the same model you train, thats why the loss on start is quite low already. So basically you did a epochs=19 run

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:33:00 - 06:52:08
overfitting example : - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

overfitting example :

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:34:42 - 06:52:08
Just a heads up, there was a mistake at  - you forgot to reinitialize the "model"! - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Just a heads up, there was a mistake at - you forgot to reinitialize the "model"!

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:35:00 - 06:52:08
When you were fitting the model around , it looked like each run of model.fit() was actually running from the previously fit model, rather than on a fresh, unfit model. So essentially the first run had a total of 10 epochs, then 18, then 19, rather than 10, 8, and 1 like you seemed to think. I don't know if you caught this later in the video, but it didn't seem like you did here. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

When you were fitting the model around , it looked like each run of model.fit() was actually running from the previously fit model, rather than on a fresh, unfit model. So essentially the first run had a total of 10 epochs, then 18, then 19, rather than 10, 8, and 1 like you seemed to think. I don't know if you caught this later in the video, but it didn't seem like you did here.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:35:00 - 06:52:08
You said, epoch used to split big dataset into small one. but at  , you have 60k images dataset and each epoch processing 60k images. how?? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

You said, epoch used to split big dataset into small one. but at , you have 60k images dataset and each epoch processing 60k images. how??

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:35:00 - 06:52:08
Just a comment, on , when you train the network again it's not re-training from scratch but instead using the weights it already had. Unless you manually reset the graph, you'll be training for the sum of all epochs you used the fit function (like 10 + 8 + 1 epochs)To avoid this problem you should use something like keras.backend.clear_session() or tf.reset_default_graph() between tests with hyperparameters 😉 - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Just a comment, on , when you train the network again it's not re-training from scratch but instead using the weights it already had. Unless you manually reset the graph, you'll be training for the sum of all epochs you used the fit function (like 10 + 8 + 1 epochs)To avoid this problem you should use something like keras.backend.clear_session() or tf.reset_default_graph() between tests with hyperparameters 😉

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:36:44 - 06:52:08
At , when you said with less epochs, you are getting better test results. Which is actually not the case. You first run for 10 epochs, your weights got updated. Then again you run 8 epochs, your weights improved from previous values onwards.. so that eventually makes 18 epochs.. then you run for 1 epoch, which makes it 19 epochs.. so in this case, after 19th epoch, your accuracy on test data is increased. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

At , when you said with less epochs, you are getting better test results. Which is actually not the case. You first run for 10 epochs, your weights got updated. Then again you run 8 epochs, your weights improved from previous values onwards.. so that eventually makes 18 epochs.. then you run for 1 epoch, which makes it 19 epochs.. so in this case, after 19th epoch, your accuracy on test data is increased.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:37:00 - 06:52:08
Not sure if it is already discussed or not. At  you updated the model by running the same cell for multiple epochs. There the previous model got updated and thus accuracy improved. Not that, with less epoch the accuracy is high. Thanks :) - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Not sure if it is already discussed or not. At you updated the model by running the same cell for multiple epochs. There the previous model got updated and thus accuracy improved. Not that, with less epoch the accuracy is high. Thanks :)

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:37:00 - 06:52:08
Okay I'm not completely sure about this so take it with a grain of salt but I don't think you're hyperparameter/epoch tuning at  is doing what you expect. With jupyter notebooks, it saves the models and each time you run an epoch, it continues tuning the previous weights. In order to really display epoch differences, you need to restart the runtime and repeat the process. If you notice, each time you run the code, the "first epoch accuracy" increases significantly. The first time you ran it, the accuracy was 83% after the first epoch. After the 10th, it was 90.6%. Then, for the next iteration (8 epochs), the accuracy was 91.2% after the first epoch. Then, when running on just a single epoch, it started at 93%. Likely this is because the model continued to train an additional 9 epochs. So, in fact, the single epoch data is ironically quite overfitting. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Okay I'm not completely sure about this so take it with a grain of salt but I don't think you're hyperparameter/epoch tuning at is doing what you expect. With jupyter notebooks, it saves the models and each time you run an epoch, it continues tuning the previous weights. In order to really display epoch differences, you need to restart the runtime and repeat the process. If you notice, each time you run the code, the "first epoch accuracy" increases significantly. The first time you ran it, the accuracy was 83% after the first epoch. After the 10th, it was 90.6%. Then, for the next iteration (8 epochs), the accuracy was 91.2% after the first epoch. Then, when running on just a single epoch, it started at 93%. Likely this is because the model continued to train an additional 9 epochs. So, in fact, the single epoch data is ironically quite overfitting.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:37:00 - 06:52:08
Run time  - I think we have to compile the model every time before we do a fit. Otherwise it just memorize the previous epochs and use it for next iterations. In this case I believe that 92% accuracy of 1 epochs is the same as the addition of previous epochs i.e 10+8+1 = 19 epochs - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Run time - I think we have to compile the model every time before we do a fit. Otherwise it just memorize the previous epochs and use it for next iterations. In this case I believe that 92% accuracy of 1 epochs is the same as the addition of previous epochs i.e 10+8+1 = 19 epochs

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:37:00 - 06:52:08
Training on one epoch in this case builds on already existing model that was created using many epochs. You need to recreate the model to demonstrate this - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Training on one epoch in this case builds on already existing model that was created using many epochs. You need to recreate the model to demonstrate this

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:37:01 - 06:52:08
At ... actually the code has run for  19 times on the train data and three time on test data... to check it over need to reset model or reset runtime... - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

At ... actually the code has run for 19 times on the train data and three time on test data... to check it over need to reset model or reset runtime...

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:37:10 - 06:52:08
On  you need to reset your NN by running again the code that defines it, and then train it using less epochs. Otherwise the epochs just accumulate. This is why you see an increasing accuracy every time you run. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

On you need to reset your NN by running again the code that defines it, and then train it using less epochs. Otherwise the epochs just accumulate. This is why you see an increasing accuracy every time you run.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:37:11 - 06:52:08
For some reason it appears 7/7 when im trainning the model but my dataset is 200, and your is 60,000 and it clearly shows you 60000/60000 - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

For some reason it appears 7/7 when im trainning the model but my dataset is 200, and your is 60,000 and it clearly shows you 60000/60000

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:37:20 - 06:52:08
I think you just trained the previously trained model. - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

I think you just trained the previously trained model.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:37:28 - 06:52:08
Hi great course. However I noticed one explanation that is not correct regarding over fitting at time dnn example : over fitting vs. number of epochs. (you tried 10 then 8 then 1 epoch) . Actually that is not correct as each time you run , you continue from the last checkpoint . So first time you have epoch 0-10, then 10-18 and finally 18-19. Hope it helps! - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Hi great course. However I noticed one explanation that is not correct regarding over fitting at time dnn example : over fitting vs. number of epochs. (you tried 10 then 8 then 1 epoch) . Actually that is not correct as each time you run , you continue from the last checkpoint . So first time you have epoch 0-10, then 10-18 and finally 18-19. Hope it helps!

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:37:40 - 06:52:08
At   ... why the prediction = model.prediction(np.array([image])) instead of prediction  = model.prediction(image) ?. The image are already in array right if im not mistaken - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

At ... why the prediction = model.prediction(np.array([image])) instead of prediction = model.prediction(image) ?. The image are already in array right if im not mistaken

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:42:10 - 06:52:08
I run this code, but it doesn't give me expected and guess. It just give me the picture. Why? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

I run this code, but it doesn't give me expected and guess. It just give me the picture. Why?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:42:43 - 06:52:08
: Deep Computer Vision - Convolutional Neural Networks () - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

: Deep Computer Vision - Convolutional Neural Networks ()

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:43:10 - 04:40:44
: Deep Computer Vision - Convolutional Neural Networks (​) - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

: Deep Computer Vision - Convolutional Neural Networks (​)

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:43:10 - 04:40:44
⌨️ () Module 5: Deep Computer Vision - Convolutional Neural Networks - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

⌨️ () Module 5: Deep Computer Vision - Convolutional Neural Networks

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
03:43:10 - 04:40:44
Why are we using a activation function in CNN, we use pooling to reduce the dimension but an activation function increases the dimension as was said before ? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Why are we using a activation function in CNN, we use pooling to reduce the dimension but an activation function increases the dimension as was said before ?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
04:11:45 - 06:52:08
Can someone please tell why he has validated the data while fitting the model? Any significance? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Can someone please tell why he has validated the data while fitting the model? Any significance?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
04:16:30 - 06:52:08
what is disable progress bar - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

what is disable progress bar

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
04:25:30 - 06:52:08
Could anyone help me, telling me which is the color correction function he is talking about at  and where it should be placed? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Could anyone help me, telling me which is the color correction function he is talking about at and where it should be placed?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
04:28:15 - 06:52:08
Does somebody know the missing code -> which messes up the color at ? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Does somebody know the missing code -> which messes up the color at ?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
04:28:38 - 06:52:08
Can someone explain to me where the number 32 comes from? The note says "The 32 means that we have 32 layers of differnt filters/features." Why is that? My understanding is that the size here (32,5,5,1280) has only to do with the last layer. Why Tim uses "layers" in his note? Thanks a lot! - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Can someone explain to me where the number 32 comes from? The note says "The 32 means that we have 32 layers of differnt filters/features." Why is that? My understanding is that the size here (32,5,5,1280) has only to do with the last layer. Why Tim uses "layers" in his note? Thanks a lot!

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
04:31:00 - 06:52:08
At , Tim said that the no of trainable parameters is zero because we get .trainable = False , but its value is already zero if you see the output before.Can someone explain this to me ?Thanks a lot in advance - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

At , Tim said that the no of trainable parameters is zero because we get .trainable = False , but its value is already zero if you see the output before.Can someone explain this to me ?Thanks a lot in advance

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
04:32:24 - 06:52:08
: Natural Language Processing with RNNs () - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

: Natural Language Processing with RNNs ()

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
04:40:44 - 06:08:00
: Natural Language Processing with RNNs (​) - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

: Natural Language Processing with RNNs (​)

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
04:40:44 - 06:08:00
⌨️ () Module 6: Natural Language Processing with RNNs - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

⌨️ () Module 6: Natural Language Processing with RNNs

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
04:40:44 - 06:08:00
. how did you get VOCAB_SIZE? - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

. how did you get VOCAB_SIZE?

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
05:13:41 - 06:52:08
. We can rebuild a model and change its original parameters - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

. We can rebuild a model and change its original parameters

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
05:56:31 - 06:08:00
. Reinforcement learning with Q-Learning - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

. Reinforcement learning with Q-Learning

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
06:08:00 - 06:52:08
: Reinforcement Learning with Q-Learning () - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

: Reinforcement Learning with Q-Learning ()

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
06:08:00 - 06:48:24
: Reinforcement Learning with Q-Learning (​) - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

: Reinforcement Learning with Q-Learning (​)

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
06:08:00 - 06:48:24
⌨️ () Module 7: Reinforcement Learning with Q-Learning - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

⌨️ () Module 7: Reinforcement Learning with Q-Learning

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
06:08:00 - 06:48:24
Timestamp for me: - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

Timestamp for me:

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
06:30:00 - 06:52:08
, dont update the Q table? which means we use pretrain table? dont write that line of code? Or you are saying something else.  I am not sure what it mean - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

, dont update the Q table? which means we use pretrain table? dont write that line of code? Or you are saying something else. I am not sure what it mean

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
06:47:38 - 06:52:08
: Conclusion and Next Steps () - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

: Conclusion and Next Steps ()

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
06:48:24 - 06:52:08
: Conclusion and Next Steps (​) - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

: Conclusion and Next Steps (​)

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
06:48:24 - 06:52:08
⌨️ () Module 8: Conclusion and Next Steps - TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial

⌨️ () Module 8: Conclusion and Next Steps

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
2020年03月04日
06:48:24 - 06:52:08
freeCodeCamp.org

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【予測】700万人まであと70日(2022年12月16日)

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Timetable

動画タイムテーブル

動画数:47件

⌨️ () Intro - Data Analysis with Python for Excel Users - Full Course

⌨️ () Intro

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
00:00:00 - 00:01:48
⌨️ () Install Python and Jupyter Notebook with Anaconda - Data Analysis with Python for Excel Users - Full Course

⌨️ () Install Python and Jupyter Notebook with Anaconda

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
00:01:48 - 00:03:53
⌨️ () Jupyter Notebook Interface - Data Analysis with Python for Excel Users - Full Course

⌨️ () Jupyter Notebook Interface

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
00:03:53 - 00:13:56
⌨️ () Cell Types and Cell Mode - Data Analysis with Python for Excel Users - Full Course

⌨️ () Cell Types and Cell Mode

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
00:13:56 - 00:21:34
⌨️ () Jupyter Notebook Shortcuts - Data Analysis with Python for Excel Users - Full Course

⌨️ () Jupyter Notebook Shortcuts

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
00:21:34 - 00:26:39
⌨️ () Module 1 - Hello World - Data Analysis with Python for Excel Users - Full Course

⌨️ () Module 1 - Hello World

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
00:26:39 - 00:30:30
⌨️ () Data Type - Data Analysis with Python for Excel Users - Full Course

⌨️ () Data Type

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
00:30:30 - 00:39:10
Like the way you have explained the concepts - thanks Frank.  Not sure if I missed out, but I could not find anything related to tuples between the timestamp  to 1:21:50.  Is it explained later? - Data Analysis with Python for Excel Users - Full Course

Like the way you have explained the concepts - thanks Frank. Not sure if I missed out, but I could not find anything related to tuples between the timestamp to 1:21:50. Is it explained later?

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
00:39:10 - 03:57:46
⌨️ () Variables - Data Analysis with Python for Excel Users - Full Course

⌨️ () Variables

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
00:39:10 - 00:46:53
⌨️ () Lists - Data Analysis with Python for Excel Users - Full Course

⌨️ () Lists

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
00:46:53 - 01:11:14
⌨️ () Dictionaries - Data Analysis with Python for Excel Users - Full Course

⌨️ () Dictionaries

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
01:11:14 - 01:21:50
⌨️ () IF Statement - Data Analysis with Python for Excel Users - Full Course

⌨️ () IF Statement

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
01:21:50 - 01:28:04
⌨️ () FOR Loop - Data Analysis with Python for Excel Users - Full Course

⌨️ () FOR Loop

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
01:28:04 - 01:33:49
⌨️ () Functions - Data Analysis with Python for Excel Users - Full Course

⌨️ () Functions

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
01:33:49 - 01:40:59
⌨️ () Modules - Data Analysis with Python for Excel Users - Full Course

⌨️ () Modules

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
01:40:59 - 01:44:41
⌨️ () Module 2 -Introduction to Pandas - Data Analysis with Python for Excel Users - Full Course

⌨️ () Module 2 -Introduction to Pandas

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
01:44:41 - 01:51:08
⌨️ () How to create a dataframe - Data Analysis with Python for Excel Users - Full Course

⌨️ () How to create a dataframe

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
01:51:08 - 02:07:34
⌨️ () How to show a dataframe - Data Analysis with Python for Excel Users - Full Course

⌨️ () How to show a dataframe

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
02:07:34 - 02:14:24
⌨️ () Basic Attributes, Functions and Methods - Data Analysis with Python for Excel Users - Full Course

⌨️ () Basic Attributes, Functions and Methods

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
02:14:24 - 02:26:17
⌨️ () Selecting One Column from a Dataframe - Data Analysis with Python for Excel Users - Full Course

⌨️ () Selecting One Column from a Dataframe

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
02:26:17 - 02:32:13
⌨️ () Selecting Two or More Columns from a Dataframe - Data Analysis with Python for Excel Users - Full Course

⌨️ () Selecting Two or More Columns from a Dataframe

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
02:32:13 - 02:37:50
⌨️ () Add New Column to a Dataframe (Simple Assignment) - Data Analysis with Python for Excel Users - Full Course

⌨️ () Add New Column to a Dataframe (Simple Assignment)

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
02:37:50 - 02:47:51
⌨️ () Operations in dataframes - Data Analysis with Python for Excel Users - Full Course

⌨️ () Operations in dataframes

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
02:47:51 - 02:56:04
Excuse me, off topic question, I just want to know how did you add the bracket symbols and the single quote symbols to both sides of the selected string, like in ? Btw, thank you so much. The course is helpful and easy to follow for such a new data learner like me. - Data Analysis with Python for Excel Users - Full Course

Excuse me, off topic question, I just want to know how did you add the bracket symbols and the single quote symbols to both sides of the selected string, like in ? Btw, thank you so much. The course is helpful and easy to follow for such a new data learner like me.

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
02:53:04 - 03:57:46
⌨️ () The value_counts() method - Data Analysis with Python for Excel Users - Full Course

⌨️ () The value_counts() method

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
02:56:04 - 03:00:16
⌨️ () Sort a DataFrame with the sort_values() method - Data Analysis with Python for Excel Users - Full Course

⌨️ () Sort a DataFrame with the sort_values() method

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
03:00:16 - 03:09:56
@ how did you open that ... not sure what to call it ... dialog box? drop down? - Data Analysis with Python for Excel Users - Full Course

@ how did you open that ... not sure what to call it ... dialog box? drop down?

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
03:02:43 - 03:57:46
I didn't understand the use of the lambda function in the ordering section (), as you are trying to lower() the values but none of those are becoming lower. Could you explain deeper what failed there? - Data Analysis with Python for Excel Users - Full Course

I didn't understand the use of the lambda function in the ordering section (), as you are trying to lower() the values but none of those are becoming lower. Could you explain deeper what failed there?

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
03:09:40 - 03:57:46
⌨️ () Module 3: Introduction to Pivot Tables in Pandas - Data Analysis with Python for Excel Users - Full Course

⌨️ () Module 3: Introduction to Pivot Tables in Pandas

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
03:09:56 - 03:14:42
⌨️ () The pivot() method - Data Analysis with Python for Excel Users - Full Course

⌨️ () The pivot() method

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
03:14:42 - 03:20:49
⌨️ () The pivot_table() method - Data Analysis with Python for Excel Users - Full Course

⌨️ () The pivot_table() method

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
03:20:49 - 03:29:00
hahahahahahahahahahah Men spend MORE money on beauty (and health) than women :P . This can not be true  :D :D :D - Data Analysis with Python for Excel Users - Full Course

hahahahahahahahahahah Men spend MORE money on beauty (and health) than women :P . This can not be true :D :D :D

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
03:28:58 - 03:57:46
⌨️ () Data Visualization with Pandas (New Dataset + Pivot Table) - Data Analysis with Python for Excel Users - Full Course

⌨️ () Data Visualization with Pandas (New Dataset + Pivot Table)

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
03:29:00 - 03:38:38
⌨️ () Lineplot - Data Analysis with Python for Excel Users - Full Course

⌨️ () Lineplot

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
03:38:38 - 03:43:05
⌨️ () Barplot - Data Analysis with Python for Excel Users - Full Course

⌨️ () Barplot

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
03:43:05 - 03:50:52
⌨️ () Piechart - Data Analysis with Python for Excel Users - Full Course

⌨️ () Piechart

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
03:50:52 - 03:54:36
⌨️ () Save Plot and Export Pivot Table - Data Analysis with Python for Excel Users - Full Course

⌨️ () Save Plot and Export Pivot Table

Data Analysis with Python for Excel Users - Full Course
2021年11月25日
03:54:36 - 03:57:46
⌨️ () Introduction - R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps

⌨️ () Introduction

R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps
2021年09月22日
00:00:00 - 00:01:13
⌨️ () Introduction to Shiny - R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps

⌨️ () Introduction to Shiny

R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps
2021年09月22日
00:01:13 - 00:08:24
⌨️ () App 1 – Print User Input - R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps

⌨️ () App 1 – Print User Input

R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps
2021年09月22日
00:08:24 - 00:21:12
⌨️ () App 2 – Display Histogram - R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps

⌨️ () App 2 – Display Histogram

R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps
2021年09月22日
00:21:12 - 00:32:07
⌨️ () App 3 – Machine Learning (Weather Dataset) - R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps

⌨️ () App 3 – Machine Learning (Weather Dataset)

R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps
2021年09月22日
00:32:07 - 00:47:51
⌨️ () App 4 – Machine Learning (Iris Dataset) - R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps

⌨️ () App 4 – Machine Learning (Iris Dataset)

R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps
2021年09月22日
00:47:51 - 01:05:03
⌨️ () App 5 – BMI Calculator - R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps

⌨️ () App 5 – BMI Calculator

R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps
2021年09月22日
01:05:03 - 01:19:18
⌨️ () Deploy Shiny Apps to Heroku - R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps

⌨️ () Deploy Shiny Apps to Heroku

R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps
2021年09月22日
01:19:18 - 01:26:19
⌨️ () Installing Python and Jupyter - Data Analytics Crash Course: Teach Yourself in 30 Days

⌨️ () Installing Python and Jupyter

Data Analytics Crash Course: Teach Yourself in 30 Days
2021年06月16日
00:06:50 - 00:09:35
⌨️ () Working with the Jupyter environment - Data Analytics Crash Course: Teach Yourself in 30 Days

⌨️ () Working with the Jupyter environment

Data Analytics Crash Course: Teach Yourself in 30 Days
2021年06月16日
00:09:35 - 00:12:05
⌨️ () Finding data sources and using APIs - Data Analytics Crash Course: Teach Yourself in 30 Days

⌨️ () Finding data sources and using APIs

Data Analytics Crash Course: Teach Yourself in 30 Days
2021年06月16日
00:12:05 - 00:16:35
⌨️ () Working with data - Data Analytics Crash Course: Teach Yourself in 30 Days

⌨️ () Working with data

Data Analytics Crash Course: Teach Yourself in 30 Days
2021年06月16日
00:16:35 - 00:24:45
⌨️ () Plotting data - Data Analytics Crash Course: Teach Yourself in 30 Days

⌨️ () Plotting data

Data Analytics Crash Course: Teach Yourself in 30 Days
2021年06月16日
00:24:45 - 00:32:45
⌨️ () Understanding data - Data Analytics Crash Course: Teach Yourself in 30 Days

⌨️ () Understanding data

Data Analytics Crash Course: Teach Yourself in 30 Days
2021年06月16日
00:32:45 - 00:38:19
⌨️ () Introduction - Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis

⌨️ () Introduction

Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis
2021年06月02日
00:00:00 - 00:04:29
][]bartrim = fastq_obj[1][5:] # to trim sequence barcodedata = clinical_data.loc[clinical_data.Barcode==sequence] #Here we loop thru fastq - Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis

][]bartrim = fastq_obj[1][5:] # to trim sequence barcodedata = clinical_data.loc[clinical_data.Barcode==sequence] #Here we loop thru fastq

Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis
2021年06月02日
00:00:05 - 01:42:54
⌨️ () Part 1 - Data collection - Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis

⌨️ () Part 1 - Data collection

Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis
2021年06月02日
00:04:29 - 00:26:57
? - Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis

?

Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis
2021年06月02日
00:09:11 - 01:42:54
its a great video really helpful. at  you are not able to see other types of standard type variable because youve already entered its filter  as ic 50 so it shows only ic50 entries. once you remove filter.standard type[ic50] , youll see array with inhibition, ki, ec 50, kd, activity. - Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis

its a great video really helpful. at you are not able to see other types of standard type variable because youve already entered its filter as ic 50 so it shows only ic50 entries. once you remove filter.standard type[ic50] , youll see array with inhibition, ki, ec 50, kd, activity.

Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis
2021年06月02日
00:14:09 - 01:42:54
⌨️ () Part 2 - Exploratory data analysis - Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis

⌨️ () Part 2 - Exploratory data analysis

Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis
2021年06月02日
00:26:57 - 00:49:41
Somehow when I follow along, from  I get "NaN" for items 128-132 in the bioactivity class column instead of "inactive". This throws off my results for the rest of the procedures. Does anyone know how I can fix this? - Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis

Somehow when I follow along, from I get "NaN" for items 128-132 in the bioactivity class column instead of "inactive". This throws off my results for the rest of the procedures. Does anyone know how I can fix this?

Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis
2021年06月02日
00:37:19 - 01:42:54
Hi Master, thank you for this great video!!!!!!!!! in the minute  there is a cut that not allow to understand  the idea - Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis

Hi Master, thank you for this great video!!!!!!!!! in the minute there is a cut that not allow to understand the idea

Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis
2021年06月02日
00:46:59 - 01:42:54
⌨️ () Part 3 - Descriptor calculation - Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis

⌨️ () Part 3 - Descriptor calculation

Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis
2021年06月02日
00:49:41 - 01:01:51
⌨️ () Part 4 - Model building - Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis

⌨️ () Part 4 - Model building

Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis
2021年06月02日
01:01:51 - 01:10:41
⌨️ () Part 5 - Model comparison - Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis

⌨️ () Part 5 - Model comparison

Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis
2021年06月02日
01:10:41 - 01:18:15
⌨️ () Part 6 - Model deployment - Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis

⌨️ () Part 6 - Model deployment

Python for Bioinformatics - Drug Discovery Using Machine Learning and Data Analysis
2021年06月02日
01:18:15 - 01:42:54
⌨️ () introduction - Scikit-learn Crash Course - Machine Learning Library for Python

⌨️ () introduction

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
00:00:00 - 00:03:08
i did not underestand why after changing k value from 5 to 1 prediction diagram changed ? knn  is a classification algoithm but here it was like a regration - Scikit-learn Crash Course - Machine Learning Library for Python

i did not underestand why after changing k value from 5 to 1 prediction diagram changed ? knn is a classification algoithm but here it was like a regration

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
00:00:19 - 02:09:22
⌨️ () introducing scikit-learn - Scikit-learn Crash Course - Machine Learning Library for Python

⌨️ () introducing scikit-learn

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
00:03:08 - 00:34:36
pipe - Scikit-learn Crash Course - Machine Learning Library for Python

pipe

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
00:16:00 - 00:23:45
How do you do what he did at  with jupyter? - Scikit-learn Crash Course - Machine Learning Library for Python

How do you do what he did at with jupyter?

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
00:18:54 - 02:09:22
grid search - Scikit-learn Crash Course - Machine Learning Library for Python

grid search

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
00:23:45 - 00:37:00
using space instead of tab .... stops watching :) (joke) great video - Scikit-learn Crash Course - Machine Learning Library for Python

using space instead of tab .... stops watching :) (joke) great video

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
00:25:50 - 02:09:22
Yikes! Big Yikes! - Scikit-learn Crash Course - Machine Learning Library for Python

Yikes! Big Yikes!

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
00:30:59 - 02:09:22
fire statement!! - Scikit-learn Crash Course - Machine Learning Library for Python

fire statement!!

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
00:31:31 - 02:09:22
⌨️ () preprocessing - Scikit-learn Crash Course - Machine Learning Library for Python

⌨️ () preprocessing

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
00:34:36 - 00:53:36
as a non-American, it is so satisfying hearing z read as 'zed' not 'zi'. lol - Scikit-learn Crash Course - Machine Learning Library for Python

as a non-American, it is so satisfying hearing z read as 'zed' not 'zi'. lol

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
00:35:56 - 02:09:22
standard scaler - Scikit-learn Crash Course - Machine Learning Library for Python

standard scaler

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
00:37:00 - 00:42:00
quantiles better - Scikit-learn Crash Course - Machine Learning Library for Python

quantiles better

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
00:42:00 - 00:46:55
… - Scikit-learn Crash Course - Machine Learning Library for Python

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
00:46:55 - 00:55:00
count vecotorizer is a really good preprocessor for that too in my opinion - Scikit-learn Crash Course - Machine Learning Library for Python

count vecotorizer is a really good preprocessor for that too in my opinion

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
00:50:00 - 02:09:22
⌨️ () metrics - Scikit-learn Crash Course - Machine Learning Library for Python

⌨️ () metrics

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
00:53:36 - 01:24:49
fraud ex - Scikit-learn Crash Course - Machine Learning Library for Python

fraud ex

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
00:55:00 - 02:09:22
what’s the answer though? - Scikit-learn Crash Course - Machine Learning Library for Python

what’s the answer though?

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
01:11:00 - 02:09:22
⌨️ () meta-estimators - Scikit-learn Crash Course - Machine Learning Library for Python

⌨️ () meta-estimators

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
01:24:49 - 01:45:34
where is that make_plots function from, at - Scikit-learn Crash Course - Machine Learning Library for Python

where is that make_plots function from, at

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
01:31:00 - 02:09:22
⌨️ () human-learn - Scikit-learn Crash Course - Machine Learning Library for Python

⌨️ () human-learn

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
01:45:34 - 02:06:17
Great video ! At  you could use ".values" at the end instead of np.array in the beginning. - Scikit-learn Crash Course - Machine Learning Library for Python

Great video ! At you could use ".values" at the end instead of np.array in the beginning.

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
01:49:40 - 02:09:22
⌨️ () wrap-up - Scikit-learn Crash Course - Machine Learning Library for Python

⌨️ () wrap-up

Scikit-learn Crash Course - Machine Learning Library for Python
2021年04月08日
02:06:17 - 02:09:22
Course Introduction - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Course Introduction

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
00:00:00 - 00:01:42
Python Programming Fundamentals - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Python Programming Fundamentals

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
00:01:42 - 00:02:40
Course Curriculum - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Course Curriculum

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
00:02:40 - 00:05:24
Notebook - First Steps with Python and Jupyter - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Notebook - First Steps with Python and Jupyter

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
00:05:24 - 00:08:30
Performing Arithmetic Operations with Python - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Performing Arithmetic Operations with Python

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
00:08:30 - 00:11:34
Solving Multi-step problems using variables - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Solving Multi-step problems using variables

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
00:11:34 - 00:20:17
@Nijaguna Darshan I got it from the problem I read @ . It says "A grocery store SELLS an ice bag for $1.25".You don't SELL goods at COST. Then it again said that, the profit is 20%.If the SELLING price is $1.25 and 20% is profit, then 80% should be its COST.Now if you multiply $1.25 with 80% you will get $1.00, don't you.? - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

@Nijaguna Darshan I got it from the problem I read @ . It says "A grocery store SELLS an ice bag for $1.25".You don't SELL goods at COST. Then it again said that, the profit is 20%.If the SELLING price is $1.25 and 20% is profit, then 80% should be its COST.Now if you multiply $1.25 with 80% you will get $1.00, don't you.?

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
00:12:00 - 09:56:23
I think there was a typo error in your very first grocery store Profit working @ . The price is 1.25 not the cost of the item as you typed. The cost will be 1.00.Or maybe I missed something 🤔 - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

I think there was a typo error in your very first grocery store Profit working @ . The price is 1.25 not the cost of the item as you typed. The cost will be 1.00.Or maybe I missed something 🤔

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
00:12:00 - 09:56:23
Combining conditions with Logical operators - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Combining conditions with Logical operators

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
00:20:17 - 00:22:22
Adding text using Markdown - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Adding text using Markdown

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
00:22:22 - 00:23:50
Saving and Uploading to Jovian - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Saving and Uploading to Jovian

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
00:23:50 - 00:26:38
Variables and Datatypes in Python - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Variables and Datatypes in Python

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
00:26:38 - 00:31:28
Built-in Data types in Python - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Built-in Data types in Python

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
00:31:28 - 01:07:19
Further Reading - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Further Reading

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
01:07:19 - 01:08:46
Branching Loops and Functions - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Branching Loops and Functions

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
01:08:46 - 01:09:02
Notebook - Branching using conditional  statements and loops in Python - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Notebook - Branching using conditional statements and loops in Python

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
01:09:02 - 01:09:24
Branching with if, else, elif - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Branching with if, else, elif

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
01:09:24 - 01:15:25
Non Boolean conditions - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Non Boolean conditions

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
01:15:25 - 01:19:00
Iteration with while loops - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Iteration with while loops

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
01:19:00 - 01:28:57
My timestamp: - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

My timestamp:

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
01:21:00 - 09:56:23
solution for  anyone ? - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

solution for anyone ?

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
01:23:42 - 09:56:23
Iteration with for loops - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Iteration with for loops

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
01:28:57 - 01:36:27
Functions and scope in Python - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Functions and scope in Python

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
01:36:27 - 01:36:53
Creating and using functions - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Creating and using functions

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
01:36:53 - 01:42:24
@ For clarification we refer to function variables as Parameters at the function definition and as Arguments at the function invocation (call). - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

@ For clarification we refer to function variables as Parameters at the function definition and as Arguments at the function invocation (call).

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
01:39:48 - 09:56:23
Writing great functions in Python - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Writing great functions in Python

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
01:42:24 - 01:45:38
Local variables and scope - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Local variables and scope

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
01:45:38 - 02:08:19
puting a time stamp at @ so I can remember XD - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

puting a time stamp at @ so I can remember XD

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
02:00:00 - 09:56:23
Documentation functions using Docstrings - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Documentation functions using Docstrings

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
02:08:19 - 02:11:40
Exercise - Data Analysis for Vacation Planning - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Exercise - Data Analysis for Vacation Planning

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
02:11:40 - 02:17:17
Numercial Computing with Numpy - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Numercial Computing with Numpy

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
02:17:17 - 02:18:00
Notebook - Numerical Computing with Numpy - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Notebook - Numerical Computing with Numpy

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
02:18:00 - 02:26:09
pokemon fans got the reference - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

pokemon fans got the reference

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
02:21:45 - 09:56:23
just for me: - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

just for me:

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
02:25:40 - 09:56:23
From Python Lists to Numpy Arrays - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

From Python Lists to Numpy Arrays

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
02:26:09 - 02:29:09
Operating on Numpy Arrays - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Operating on Numpy Arrays

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
02:29:09 - 02:34:33
In  if we did de np.dot gets wrong value when compared with loop routine. Did someone see this? - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

In if we did de np.dot gets wrong value when compared with loop routine. Did someone see this?

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
02:33:09 - 09:56:23
at  min in this videomy np.dot product is coming in negative but while doing by zip function the answer is correct.and if i copy paste from the notes provided by still answer is note same. and under 1000 range answer is giving exact values - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

at min in this videomy np.dot product is coming in negative but while doing by zip function the answer is correct.and if i copy paste from the notes provided by still answer is note same. and under 1000 range answer is giving exact values

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
02:33:16 - 09:56:23
Multidimensional Numpy Arrays - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Multidimensional Numpy Arrays

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
02:34:33 - 03:03:41
Array Indexing and Slicing - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Array Indexing and Slicing

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
03:03:41 - 03:17:49
My timeline : - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

My timeline :

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
03:03:41 - 09:56:23
I am at @ - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

I am at @

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
03:03:50 - 09:56:23
"if you don't know these terms - don't worry about it" XD LMAO - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

"if you don't know these terms - don't worry about it" XD LMAO

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
03:14:07 - 09:56:23
Exercises and Further Reading - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Exercises and Further Reading

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
03:17:49 - 03:20:50
Assignment 2 - Numpy Array Operations - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Assignment 2 - Numpy Array Operations

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
03:20:50 - 03:29:16
100 Numpy Exercises - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

100 Numpy Exercises

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
03:29:16 - 03:31:25
Reading from and Writing to Files using Python - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Reading from and Writing to Files using Python

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
03:31:25 - 04:02:59
- The 104 Dislikers start to bark in the background. - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

- The 104 Dislikers start to bark in the background.

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
03:47:48 - 09:56:23
Analysing Tabular Data with Pandas - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Analysing Tabular Data with Pandas

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
04:02:59 - 04:03:58
Notebook - Analyzing Tabular Data with Pandas - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Notebook - Analyzing Tabular Data with Pandas

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
04:03:58 - 04:16:33
Retrieving Data from a Data Frame - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Retrieving Data from a Data Frame

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
04:16:33 - 04:32:00
Analyzing Data from Data Frames - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Analyzing Data from Data Frames

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
04:32:00 - 04:36:27
Querying and Sorting Rows - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Querying and Sorting Rows

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
04:36:27 - 05:01:45
Grouping and Aggregation - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Grouping and Aggregation

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
05:01:45 - 05:11:26
Merging Data from Multiple Sources - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Merging Data from Multiple Sources

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
05:11:26 - 05:26:00
Basic Plotting with Pandas - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Basic Plotting with Pandas

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
05:26:00 - 05:38:27
Assignment 3 - Pandas Practice - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Assignment 3 - Pandas Practice

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
05:38:27 - 05:52:48
Visualization with Matplotlib and Seaborn - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Visualization with Matplotlib and Seaborn

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
05:52:48 - 05:54:04
Notebook - Data Visualization with Matplotlib and Seaborn - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Notebook - Data Visualization with Matplotlib and Seaborn

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
05:54:04 - 06:06:43
Line Charts - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Line Charts

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
06:06:43 - 06:11:27
Improving Default Styles with Seaborn - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Improving Default Styles with Seaborn

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
06:11:27 - 06:16:51
Scatter Plots - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Scatter Plots

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
06:16:51 - 06:28:14
Histogram - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Histogram

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
06:28:14 - 06:38:47
Bar Chart - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Bar Chart

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
06:38:47 - 06:50:00
Heatmap - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Heatmap

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
06:50:00 - 06:57:08
Please HELP!!!!! At  , when I am creating the pivot table, the months are in ascending order alphabetically. How can I convert them into the custom order Jan, Feb, March.....? - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Please HELP!!!!! At , when I am creating the pivot table, the months are in ascending order alphabetically. How can I convert them into the custom order Jan, Feb, March.....?

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
06:52:45 - 09:56:23
Displaying Images with Matplotlib - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Displaying Images with Matplotlib

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
06:57:08 - 07:03:37
Plotting multiple charts in a grid - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Plotting multiple charts in a grid

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
07:03:37 - 07:15:42
References and further reading - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

References and further reading

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
07:15:42 - 07:20:17
Course Project - Exploratory Data Analysis - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Course Project - Exploratory Data Analysis

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
07:20:17 - 07:49:56
Exploratory Data Analysis - A Case Study - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Exploratory Data Analysis - A Case Study

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
07:49:56 - 07:50:55
Notebook - Exploratory Data Analysis - A case Study - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Notebook - Exploratory Data Analysis - A case Study

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
07:50:55 - 08:04:36
Data Preparation and Cleaning - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Data Preparation and Cleaning

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
08:04:36 - 08:19:37
Exploratory Analysis and Visualization - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Exploratory Analysis and Visualization

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
08:19:37 - 08:54:02
Asking and Answering Questions - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Asking and Answering Questions

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
08:54:02 - 09:22:57
Inferences and Conclusions - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Inferences and Conclusions

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
09:22:57 - 09:25:00
References and Future Work - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

References and Future Work

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
09:25:00 - 09:29:41
Setting up and running Locally - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Setting up and running Locally

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
09:29:41 - 09:34:21
Project Guidelines - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Project Guidelines

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
09:34:21 - 09:45:00
Course Recap - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Course Recap

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
09:45:00 - 09:48:01
What to do next? - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

What to do next?

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
09:48:01 - 09:49:10
Certificate of Accomplishment - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Certificate of Accomplishment

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
09:49:10 - 09:50:11
What to do after this course? - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

What to do after this course?

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
09:50:11 - 09:52:16
Jovian Platform - Data Analysis with Python Course - Numpy, Pandas, Data Visualization

Jovian Platform

Data Analysis with Python Course - Numpy, Pandas, Data Visualization
2021年02月19日
09:52:16 - 09:56:23