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No Black Box Machine Learning Course – Learn Without Libraries

No Black Box Machine Learning Course – Learn Without Libraries

In this No Black Box Machine Learning Course in JavaScript, you will gain a deep understanding of machine learning systems by coding without relying on libraries. This unique approach not only demystifies the inner workings of machine learning but also significantly enhances software development skills. ✏️ Course created by @Radu (PhD in Computer Science) 🎥 Watch part two: https://youtu.be/3wwiOSxDAmg HOMEWORK 🏠 1st assignment spreadsheet: https://docs.google.com/spreadsheets/d/16wIddJ9jKAAvJOXPcF0gNRx39AOE9A2-mQeK6UR2qnY/edit?usp=sharing 🏠 Submit all other assignments to Radu's Discord Server: https://discord.com/invite/gJFcF5XVn9 GITHUB LINKS 💻 Drawing App: https://github.com/gniziemazity/drawing-app 💻 Data: https://github.com/gniziemazity/drawing-data 💻 Custom Chart Component: https://github.com/gniziemazity/javascript_chart 💻 Full Course Code (In Parts): https://github.com/gniziemazity/ml-course PREREQUISITES 🎥 Interpolation: https://youtu.be/J_puRs40GhM 🎥 Linear Algebra: https://youtu.be/nzyOCd9FcCA 🎥 Trigonometry: https://youtu.be/xK3vKWMFVgw LINKS 🔗 Check out the Recognizer we'll build in this course: https://radufromfinland.com/projects/ml/recognizer 🔗 Draw for Radu, Call for help video: https://youtu.be/Yw2QZ1vq2ek 🔗 Draw for Radu, Data collection tool: https://radufromfinland.com/projects/ml 🔗 Radu's Self-driving Car Course: https://www.youtube.com/playlist?list=PLB0Tybl0UNfYoJE7ZwsBQoDIG4YN9ptyY 🔗 Radu's older Machine Learning video: https://youtu.be/QXB1ytG95gs 🔗 CHART TUTORIAL (mentioned at 01:45:27): https://youtu.be/n8uCt1TSGKE 🔗 CHART CODE: https://github.com/gniziemazity/javascript_chart TOOLS 🔧 Visual Studio Code: https://code.visualstudio.com/download 🔧 Google Chrome: https://www.google.com/chrome 🔧 Node JS: https://nodejs.org/en/download (make sure you add 'node' and 'npm' to the PATH environment variables when asked!) TIMESTAMPS ⌨️(0:00:00) Introduction ⌨️(0:05:04) Drawing App ⌨️(0:46:46) Homework 1 ⌨️(0:47:05) Working with Data ⌨️(1:08:54) Data Visualizer ⌨️(1:29:52) Homework 2 ⌨️(1:30:05) Feature Extraction ⌨️(1:38:07) Scatter Plot ⌨️(1:46:12) Custom Chart ⌨️(2:01:03) Homework 3 ⌨️(2:01:35) Nearest Neighbor Classifier ⌨️(2:43:21) Homework 4 (better box) ⌨️(2:43:53) Data Scaling ⌨️(2:54:45) Homework 5 ⌨️(2:55:23) K Nearest Neighbors Classifier ⌨️(3:04:18) Homework 6 ⌨️(3:04:49) Model Evaluation ⌨️(3:21:29) Homework 7 ⌨️(3:22:01) Decision Boundaries ⌨️(3:39:26) Homework 8 ⌨️(3:39:59) Python & SkLearn ⌨️(3:50:35) Homework 9
2023年04月17日
00:00:00 - 03:51:31
How Deep Neural Networks Work - Full Course for Beginners

How Deep Neural Networks Work - Full Course for Beginners

Even if you are completely new to neural networks, this course will get you comfortable with the concepts and math behind them. Neural networks are at the core of what we are calling Artificial Intelligence today. They can seem impenetrable, even mystical, if you are trying to understand them for the first time, but they don't have to. ⭐️ Contents ⭐️ ⌨️ (0:00:00) How neural networks work ⌨️ (0:24:13) What neural networks can learn and how they learn it ⌨️ (0:51:37) How convolutional neural networks (CNNs) work ⌨️ (1:16:55) How recurrent neural networks (RNNs) and long-short-term memory (LSTM) work ⌨️ (1:42:49) Deep learning demystified ⌨️ (2:03:33) Getting closer to human intelligence through robotics ⌨️ (2:49:18) How CNNs work, in depth 🎥 Lectures by Brandon Rohrer. Check out his YouTube channel: https://www.youtube.com/user/BrandonRohrer 🔗 Find more courses from Brandon at https://end-to-end-machine-learning.teachable.com/ -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org And subscribe for new videos on technology: https://youtube.com/subscription_center?add_user=freecodecamp #machine learning #deep learning #neural networks #artificial intelligence #logistic regression #probability #math #computer science #statistics #deep learning course #deep learning tutorial #neural networks for beginners #deep neural network #convolutional neural networks #recurrent neural networks #long-short-term memory #ai
2019年04月17日
00:00:00 - 03:50:57
Harvard CS50’s Introduction to Programming with Python – Full University Course

Harvard CS50’s Introduction to Programming with Python – Full University Course

Learn Python programming from Harvard University. It dives more deeply into the design and implementation of web apps with Python, JavaScript, and SQL using frameworks like Django, React, and Bootstrap. Topics include database design, scalability, security, and user experience. You will learn how to write and use APIs, create interactive UIs, and leverage cloud services like GitHub and Heroku. This course will teach you how to read and write code as well as how to test and “debug” it. It is designed for students with or without prior programming experience who’d like to learn Python specifically. Learn about functions, arguments, and return values (oh my!); variables and types; conditionals and Boolean expressions; and loops. Learn how to handle exceptions, find and fix bugs, and write unit tests; use third-party libraries; validate and extract data with regular expressions; model real-world entities with classes, objects, methods, and properties; and read and write files. Hands-on opportunities for lots of practice. Exercises inspired by real-world programming problems. No software required except for a web browser, or you can write code on your own PC or Mac. Whereas CS50x (the original CS50 course) itself focuses on computer science more generally as well as programming with C, Python, SQL, and JavaScript, this course, aka CS50P, is entirely focused on programming with Python. You can take CS50P before CS50x, during CS50x, or after CS50x. But for an introduction to computer science itself, you should still take CS50x! 💻 Slides, source code, and more at https://cs50.harvard.edu/python ✏️ Dr. David J. Malan teaches this course. 🖥 Watch the original CS50x course: https://youtu.be/8mAITcNt710 ⭐️ Course Contents ⭐️ (00:00:00) Introduction (00:04:48) Lecture 0 - Functions, Variables (01:50:24) Lecture 1 - Conditionals (02:46:23) Lecture 2 - Loops (04:07:10) Lecture 3 - Exceptions (04:51:45) Lecture 4 - Libraries (06:09:15) Lecture 5 - Unit Tests (07:00:22) Lecture 6 - File I/O (08:32:32) Lecture 7 - Regular Expressions (10:37:35) Lecture 8 - Object-Oriented Programming (13:28:47) Lecture 9 - Et Cetera HOW TO JOIN CS50 COMMUNITIES Discord: https://discord.gg/cs50 Ed: https://cs50.harvard.edu/x/ed Facebook Group: https://www.facebook.com/groups/cs50/ Faceboook Page: https://www.facebook.com/cs50/ GitHub: https://github.com/cs50 Gitter: https://gitter.im/cs50/x Instagram: https://instagram.com/cs50 LinkedIn Group: https://www.linkedin.com/groups/7437240/ LinkedIn Page: https://www.linkedin.com/school/cs50/ Medium: https://cs50.medium.com/ Quora: https://www.quora.com/topic/CS50 Reddit: https://www.reddit.com/r/cs50/ Slack: https://cs50.edx.org/slack Snapchat: https://www.snapchat.com/add/cs50 SoundCloud: https://soundcloud.com/cs50 Stack Exchange: https://cs50.stackexchange.com/ TikTok: https://www.tiktok.com/@cs50 Twitter: https://twitter.com/cs50 YouTube: https://www.youtube.com/cs50 HOW TO FOLLOW DAVID J. MALAN Facebook: https://www.facebook.com/dmalan GitHub: https://github.com/dmalan Instagram: https://www.instagram.com/davidjmalan/ LinkedIn: https://www.linkedin.com/in/malan/ TikTok: https://www.tiktok.com/@davidjmalan Twitter: https://twitter.com/davidjmalan LICENSE CC BY-NC-SA 4.0 Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License https://creativecommons.org/licenses/by-nc-sa/4.0/
2023年05月01日
00:00:00 - 15:57:48
Deep Learning Interview Prep Course

Deep Learning Interview Prep Course

Prepare for a job interview about deep learning. This course covers 50 common interview questions related to deep learning and gives detailed explanations. ✏️ Course created by Tatev Karen Aslanyan. ✏️ Expanded course with 100 questions: https://courses.lunartech.ai/courses/deep-learning-interview-preparation-course-100-q-a-s ⭐️ Contents ⭐️ ⌨️ 0:00:00 Introduction ⌨️ 0:08:20 Question 1: What is Deep Learning? ⌨️ 0:11:45 Question 2: How does Deep Learning differ from traditional Machine Learning? ⌨️ 0:15:25 Question 3: What is a Neural Network? ⌨️ 0:21:40 Question 4: Explain the concept of a neuron in Deep Learning ⌨️ 0:24:35 Question 5: Explain architecture of Neural Networks in simple way ⌨️ 0:31:45 Question 6: What is an activation function in a Neural Network? ⌨️ 0:35:00 Question 7: Name few popular activation functions and describe them ⌨️ 0:47:40 Question 8: What happens if you do not use any activation functions in a neural network? ⌨️ 0:48:20 Question 9: Describe how training of basic Neural Networks works ⌨️ 0:53:45 Question 10: What is Gradient Descent? ⌨️ 1:03:50 Question 11: What is the function of an optimizer in Deep Learning? ⌨️ 1:09:25 Question 12: What is backpropagation, and why is it important in Deep Learning? ⌨️ 1:17:25 Question 13: How is backpropagation different from gradient descent? ⌨️ 1:19:55 Question 14: Describe what Vanishing Gradient Problem is and it’s impact on NN ⌨️ 1:25:55 Question 15: Describe what Exploding Gradients Problem is and it’s impact on NN ⌨️ 1:33:55 Question 16: There is a neuron in the hidden layer that always results in an error. What could be the reason? ⌨️ 1:37:50 Question 17: What do you understand by a computational graph? ⌨️ 1:43:28 Question 18: What is Loss Function and what are various Loss functions used in Deep Learning? ⌨️ 1:47:15 Question 19: What is Cross Entropy loss function and how is it called in industry? ⌨️ 1:50:18 Question 20: Why is Cross-entropy preferred as the cost function for multi-class classification problems? ⌨️ 1:53:10 Question 21: What is SGD and why it’s used in training Neural Networks? ⌨️ 1:58:24 Question 22: Why does stochastic gradient descent oscillate towards local minima? ⌨️ 2:03:38 Question 23: How is GD different from SGD? ⌨️ 2:08:19 Question 24: How can optimization methods like gradient descent be improved? What is the role of the momentum term? ⌨️ 2:14:22 Question 25: Compare batch gradient descent, minibatch gradient descent, and stochastic gradient descent. ⌨️ 2:19:12 Question 26: How to decide batch size in deep learning (considering both too small and too large sizes)? ⌨️ 2:26:01 Question 27: Batch Size vs Model Performance: How does the batch size impact the performance of a deep learning model? ⌨️ 2:29:33 Question 28: What is Hessian, and how can it be used for faster training? What are its disadvantages? ⌨️ 2:34:12 Question 29: What is RMSProp and how does it work? ⌨️ 2:38:43 Question 30: Discuss the concept of an adaptive learning rate. Describe adaptive learning methods ⌨️ 2:43:34 Question 31: What is Adam and why is it used most of the time in NNs? ⌨️ 2:49:59 Question 32: What is AdamW and why it’s preferred over Adam? ⌨️ 2:54:50 Question 33: What is Batch Normalization and why it’s used in NN? ⌨️ 3:03:19 Question 34: What is Layer Normalization, and why it’s used in NN? ⌨️ 3:06:20 Question 35: What are Residual Connections and their function in NN? ⌨️ 3:15:05 Question 36: What is Gradient clipping and their impact on NN? ⌨️ 3:18:09 Question 37: What is Xavier Initialization and why it’s used in NN? ⌨️ 3:22:13 Question 38: What are different ways to solve Vanishing gradients? ⌨️ 3:25:25 Question 39: What are ways to solve Exploding Gradients? ⌨️ 3:26:42 Question 40: What happens if the Neural Network is suffering from Overfitting relate to large weights? ⌨️ 3:29:18 Question 41: What is Dropout and how does it work? ⌨️ 3:33:59 Question 42: How does Dropout prevent overfitting in NN? ⌨️ 3:35:06 Question 43: Is Dropout like Random Forest? ⌨️ 3:39:21 Question 44: What is the impact of Drop Out on the training vs testing? ⌨️ 3:41:20 Question 45: What are L2/L1 Regularizations and how do they prevent overfitting in NN? ⌨️ 3:44:39 Question 46: What is the difference between L1 and L2 regularisations in NN? ⌨️ 3:48:43 Question 47: How do L1 vs L2 Regularization impact the Weights in a NN? ⌨️ 3:51:56 Question 48: What is the curse of dimensionality in ML or AI? ⌨️ 3:53:04 Question 49: How deep learning models tackle the curse of dimensionality? ⌨️ 3:56:47 Question 50: What are Generative Models, give examples?
2024年01月31日
00:00:00 - 03:59:50
Data Science Internship Program | Why Become a Data Scientist | Edureka

Data Science Internship Program | Why Become a Data Scientist | Edureka

🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py 🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚'𝐬 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐅𝐫𝐞𝐞 𝐃𝐞𝐦𝐨 𝐒𝐞𝐬𝐬𝐢𝐨𝐧: https://www.edureka.co/internship/data-science-and-machine-learning-program In this video on Data Science Internship Program, we take you through some facts which will help you understand why Data Science is the job of the century, and Why you should Become a Data Scientist. 📝Feel free to leave any queries or comments in the comment section below, we will be happy to answer📝 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 🔵 DevOps Online Training: https://bit.ly/38Fx6Cx 🌕 AWS Online Training: https://bit.ly/3DZkXDR 🔵 Azure DevOps Online Training: https://bit.ly/37s448u 🌕 Tableau Online Training: https://bit.ly/37oHjCy 🔵 Power BI Online Training: https://bit.ly/3rbpI8e 🌕 Selenium Online Training: https://bit.ly/38uou1b 🔵 PMP Online Training: https://bit.ly/3LJywtI 🌕 Salesforce Online Training: https://bit.ly/3LQzEvS 🔵 Cybersecurity Online Training: https://bit.ly/3uZkDRz 🌕 Java Online Training: https://bit.ly/3LRDN2i 🔵 Big Data Online Training: https://bit.ly/3JfoleQ 🌕 RPA Online Training: https://bit.ly/35QtmwH 🔵 Python Online Training: https://bit.ly/3DS1dC4 🌕 Azure Online Training: https://bit.ly/3v0cKLD 🔵 GCP Online Training: https://bit.ly/3jiQLKw 🌕 Microservices Online Training: https://bit.ly/37n4GMN 🔵 Data Science Online Training: https://bit.ly/3Jsy9T2 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 🔵 DevOps Engineer Masters Program: https://bit.ly/3NUTAPS 🌕 Cloud Architect Masters Program: https://bit.ly/3DUVvzy 🔵 Data Scientist Masters Program: https://bit.ly/3v6RTq9 🌕 Big Data Architect Masters Program: https://bit.ly/3uiEJXO 🔵 Machine Learning Engineer Masters Program: https://bit.ly/3Km3Iz9 🌕 Business Intelligence Masters Program: https://bit.ly/35RQ7R3 🔵 Python Developer Masters Program: https://bit.ly/3rxAPIX 🌕 RPA Developer Masters Program: https://bit.ly/3LNiHSJ 🔵 Web Development Masters Program: https://bit.ly/3ukSpBy 🌕 Computer Science Bootcamp Program : https://bit.ly/38FA9KZ 🔵 Cyber Security Masters Program: https://bit.ly/3NVqYGh 🌕 Full Stack Developer Masters Program : https://bit.ly/3LTxV93 🔵 Automation Testing Engineer Masters Program : https://bit.ly/3rxAPIX 🌕 Python Developer Masters Program : https://bit.ly/3ucD3yX 🔵 Azure Cloud Engineer Masters Program: https://bit.ly/3LTxZ8N 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝗼𝘀𝘁 𝗚𝗿𝗮𝗱𝘂𝗮𝘁𝗲 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 🌕 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3LP7h0Z 📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates 📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain 📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka 📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: https://www.instagram.com/edureka_learning/ 📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/ 📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN 📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN 📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/ 📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/ What are the prerequisites to enroll in this course? No pre-requisites are required to enroll in this Data Science & Machine Learning program. You will be taught Python programming from the very basics, then Machine learning, then Deep Learning and Tableau. All you need is to have basic computer skills, dedication and proactiveness to learn. Can I master Data Science and Machine Learning in such a short duration? Yes, the internship programs by Edureka are specially designed to make experts in the most trending fields and technologies in a short period of time. The content for this course is created by Technology Specialists, Data Science & Machine Learning Experts and Engineering Managers from top tech companies. Based on their years of experience and domain knowledge, they have chosen the most important concepts in an easily understandable format in this single course. These key concepts are fundamental to becoming a top Data Scientist and cracking interviews in top tech companies. This program will follow a similar training structure as followed by top tech companies and will also cover important details from their interviews. #data science internship #why become a data scientist #data science #edureka
2022年04月08日
00:00:00 - 00:01:27
Machine Learning & Neural Networks without Libraries – No Black Box Course

Machine Learning & Neural Networks without Libraries – No Black Box Course

Welcome to this No Black Box Machine Learning Course in JavaScript. It’s a course where we code without using libraries because it’s the best way to learn all inner workings of a machine learning system and you’ll greatly improve your software development skills as well. The goal in this course is to build a web app that learns to recognize drawings. This is phase 2, where we increase the accuracy of the method we developed in Phase 1. We do this by implementing more sophisticated features and using other classification methods (like the Neural Network). In Phase 2 we also learn about Data Cleaning, Confusion Matrices, Geometry and the difference between Vector and Raster data (pixels). 🎥 No Black Box Phase 1 Course: https://youtu.be/vDDjtwQDw2k ✏️ Course created by @Radu (PhD in Computer Science) 📁 Data: https://github.com/gniziemazity/drawing-data 💻 Code: https://github.com/gniziemazity/ml-course-phase-2 💻 Ilya's code: https://gist.github.com/id-ilych/8630fb273e5c5a0b64ca1dc080d68b63 💻 Neural Network Code: https://github.com/gniziemazity/neural-network Phase 3 Poll: https://forms.office.com/e/QTMCLLaV24 ⭐️ Other Resources ⭐️ Recognizer we build in this course: https://radufromfinland.com/projects/ml/recognizer Euclidean Distance Video: https://youtu.be/3rPwfmrCwVw Interpolation Video: https://youtu.be/J_puRs40GhM Draw the Portal Game Tutorial (Inspired from Dr. Strange): https://youtu.be/0SxiyLk2IMM Why the Circle has the Largest Area: https://youtu.be/CFBa2ezTQJQ Recognizing drawings via webcam: https://youtu.be/QXB1ytG95gs Self-driving Car Course: https://youtu.be/Rs_rAxEsAvI Discord Server: https://discord.com/invite/gJFcF5XVn9 Scikit-learn documentation: http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html ⭐️ Contents ⭐️ 0:00:00 Introduction 0:04:07 Phase 1 Code Review 0:23:11 Data Cleaning 0:41:30 Confusion Matrix 1:16:00 Euclidean Distance Marker 1:16:06 Measuring the Elongation 1:39:23 Measuring the Roundness 1:59:20 Vector vs Raster (Pixels) 2:22:40 Neural Networks 3:04:49 Optimizing Neural Networks 3:25:15 Deep Neural Networks 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan 👾 Oscar Rahnama -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news
2023年08月29日
00:00:00 - 03:37:32
PLEASE Use These 5 Python Decorators

PLEASE Use These 5 Python Decorators

In this tutorial, I'll be unveiling 5 essential Python decorators that every programmer should have in their toolkit. If you're wondering what decorators are and how they can supercharge your Python code, you're in the right place. Throughout this video, I'll dive deep into the world of decorators, explaining their fundamental principles and showcasing real-world examples of how they can streamline your code and make it more efficient. If you want to land a developer job check out my program with Course Careers: https://techwithtim.net/dev Skool community for free resources: https://www.skool.com/software-developer-academy/about Discord server: https://discord.gg/twt 🎞 Video Resources 🎞 Python Decorators Tutorial: https://www.youtube.com/watch?v=tfCz563ebsU Python Dataclasses Tutorial: https://youtu.be/5mMpM8zK4pY?si=wSuNbR5pzI3nldv9 ⏳ Timestamps ⏳ 00:00 | What Is A Decorator? 05:30 | What You NEED To Know 06:14 | @property 10:34 | @staticmethod 12:10 | @classmethod 13:45 | @functools.cache 17:09 | @dataclass Hashtags #TechWithTim #PythonDecorators #PythonProgramming #CodingTips #tech with tim #Python #decorators #Python programming #coding tips #Tech with Tim #Python tutorial #Python functions #Python decorators tutorial #Python programming guide #coding in Python #software development #Python projects #Python programming tips #Python coding #coding for beginners #Python advanced #Python decorators examples #Python development #Python functions tutorial #artificial intelligence #ai #how to code in python #coding tutorial #programming basics
2024年03月19日
00:00:00 - 00:20:12
PyTorch for Deep Learning & Machine Learning – Full Course

PyTorch for Deep Learning & Machine Learning – Full Course

Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python. ✏️ Daniel Bourke developed this course. Check out his channel: https://www.youtube.com/channel/UCr8O8l5cCX85Oem1d18EezQ 🔗 Code: https://github.com/mrdbourke/pytorch-deep-learning 🔗 Ask a question: https://github.com/mrdbourke/pytorch-deep-learning/discussions 🔗 Course materials online: https://learnpytorch.io 🔗 Full course on Zero to Mastery (20+ hours more video): https://dbourke.link/ZTMPyTorch Some sections below have been left out because of the YouTube limit for timestamps. 0:00:00 Introduction 🛠 Chapter 0 – PyTorch Fundamentals 0:01:45 0. Welcome and "what is deep learning?" 0:07:41 1. Why use machine/deep learning? 0:11:15 2. The number one rule of ML 0:16:55 3. Machine learning vs deep learning 0:23:02 4. Anatomy of neural networks 0:32:24 5. Different learning paradigms 0:36:56 6. What can deep learning be used for? 0:43:18 7. What is/why PyTorch? 0:53:33 8. What are tensors? 0:57:52 9. Outline 1:03:56 10. How to (and how not to) approach this course 1:09:05 11. Important resources 1:14:28 12. Getting setup 1:22:08 13. Introduction to tensors 1:35:35 14. Creating tensors 1:54:01 17. Tensor datatypes 2:03:26 18. Tensor attributes (information about tensors) 2:11:50 19. Manipulating tensors 2:17:50 20. Matrix multiplication 2:48:18 23. Finding the min, max, mean & sum 2:57:48 25. Reshaping, viewing and stacking 3:11:31 26. Squeezing, unsqueezing and permuting 3:23:28 27. Selecting data (indexing) 3:33:01 28. PyTorch and NumPy 3:42:10 29. Reproducibility 3:52:58 30. Accessing a GPU 4:04:49 31. Setting up device agnostic code 🗺 Chapter 1 – PyTorch Workflow 4:17:27 33. Introduction to PyTorch Workflow 4:20:14 34. Getting setup 4:27:30 35. Creating a dataset with linear regression 4:37:12 36. Creating training and test sets (the most important concept in ML) 4:53:18 38. Creating our first PyTorch model 5:13:41 40. Discussing important model building classes 5:20:09 41. Checking out the internals of our model 5:30:01 42. Making predictions with our model 5:41:15 43. Training a model with PyTorch (intuition building) 5:49:31 44. Setting up a loss function and optimizer 6:02:24 45. PyTorch training loop intuition 6:40:05 48. Running our training loop epoch by epoch 6:49:31 49. Writing testing loop code 7:15:53 51. Saving/loading a model 7:44:28 54. Putting everything together 🤨 Chapter 2 – Neural Network Classification 8:32:00 60. Introduction to machine learning classification 8:41:42 61. Classification input and outputs 8:50:50 62. Architecture of a classification neural network 9:09:41 64. Turing our data into tensors 9:25:58 66. Coding a neural network for classification data 9:43:55 68. Using torch.nn.Sequential 9:57:13 69. Loss, optimizer and evaluation functions for classification 10:12:05 70. From model logits to prediction probabilities to prediction labels 10:28:13 71. Train and test loops 10:57:55 73. Discussing options to improve a model 11:27:52 76. Creating a straight line dataset 11:46:02 78. Evaluating our model's predictions 11:51:26 79. The missing piece – non-linearity 12:42:32 84. Putting it all together with a multiclass problem 13:24:09 88. Troubleshooting a mutli-class model 😎 Chapter 3 – Computer Vision 14:00:48 92. Introduction to computer vision 14:12:36 93. Computer vision input and outputs 14:22:46 94. What is a convolutional neural network? 14:27:49 95. TorchVision 14:37:10 96. Getting a computer vision dataset 15:01:34 98. Mini-batches 15:08:52 99. Creating DataLoaders 15:52:01 103. Training and testing loops for batched data 16:26:27 105. Running experiments on the GPU 16:30:14 106. Creating a model with non-linear functions 16:42:23 108. Creating a train/test loop 17:13:32 112. Convolutional neural networks (overview) 17:21:57 113. Coding a CNN 17:41:46 114. Breaking down nn.Conv2d/nn.MaxPool2d 18:29:02 118. Training our first CNN 18:44:22 120. Making predictions on random test samples 18:56:01 121. Plotting our best model predictions 19:19:34 123. Evaluating model predictions with a confusion matrix 🗃 Chapter 4 – Custom Datasets 19:44:05 126. Introduction to custom datasets 19:59:54 128. Downloading a custom dataset of pizza, steak and sushi images 20:13:59 129. Becoming one with the data 20:39:11 132. Turning images into tensors 21:16:16 136. Creating image DataLoaders 21:25:20 137. Creating a custom dataset class (overview) 21:42:29 139. Writing a custom dataset class from scratch 22:21:50 142. Turning custom datasets into DataLoaders 22:28:50 143. Data augmentation 22:43:14 144. Building a baseline model 23:11:07 147. Getting a summary of our model with torchinfo 23:17:46 148. Creating training and testing loop functions 23:50:59 151. Plotting model 0 loss curves 24:00:02 152. Overfitting and underfitting 24:32:31 155. Plotting model 1 loss curves 24:35:53 156. Plotting all the loss curves 24:46:50 157. Predicting on custom data
2022年10月06日
00:00:00 - 25:37:26
OpenCV Course - Full Tutorial with Python

OpenCV Course - Full Tutorial with Python

Learn everything you need to know about OpenCV in this full course for beginners. You will learn the very basics (reading images and videos, image transformations) to more advanced concepts (color spaces, edge detection). Towards the end, you'll have hands-on experience building a Deep Computer Vision model to classify between the characters in the popular TV series "The Simpsons". ⭐️ Code ⭐️ 🔗Github link: https://github.com/jasmcaus/opencv-course 🔗The Caer Vision library: https://github.com/jasmcaus/caer 🎥 Course from Jason Dsouza: - Check out his Youtube channel: https://www.youtube.com/jasmcaus - Follow him on Twitter: https://twitter.com/jasmcaus ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Introduction ⌨️ (0:01:07) Installing OpenCV and Caer Section #1 - Basics ⌨️ (0:04:12) Reading Images & Video ⌨️ (0:12:57) Resizing and Rescaling Frames ⌨️ (0:20:21) Drawing Shapes & Putting Text ⌨️ (0:31:55) 5 Essential Functions in OpenCV ⌨️ (0:44:13) Image Transformations ⌨️ (0:57:06) Contour Detection Section #2 - Advanced ⌨️ (1:12:53) Color Spaces ⌨️ (1:23:10) Color Channels ⌨️ (1:31:03) Blurring ⌨️ (1:44:27) BITWISE operations ⌨️ (1:53:06) Masking ⌨️ (2:01:43) Histogram Computation ⌨️ (2:15:22) Thresholding/Binarizing Images ⌨️ (2:26:27) Edge Detection Section #3 - Faces: ⌨️ (2:35:25) Face Detection with Haar Cascades ⌨️ (2:49:05) Face Recognition with OpenCV's built-in recognizer Section #4 - Capstone ⌨️ (3:11:57) Deep Computer Vision: The Simpsons ⭐️ More ways to connect with Jason Dsouza ⭐️ - Medium: https://jasmcaus.medium.com - Twitter: https://twitter.com/jasmcaus - LinkedIn: https://www.linkedin.com/in/jasmcaus ✏️ Check out Jason's Deep Learning Crash Course for Beginners: https://youtu.be/VyWAvY2CF9c ⭐️ Special thanks to our Champion supporters! ⭐️ 🏆 Loc Do 🏆 Joseph C 🏆 DeezMaster -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news #opencv #python #Caer
2020年11月03日
00:00:00 - 03:41:42
Learn Artificial Intelligence Through Stanford's Online Professional & Graduate Programs

Learn Artificial Intelligence Through Stanford's Online Professional & Graduate Programs

Learn more about Stanford's online artificial intelligence professional and graduate programs: https://stanford.io/3CDAIOV Stanford Online Artificial Intelligence courses let you virtually step into the classrooms of Stanford professors who are leading the AI revolution. Learn from anywhere in the world, wherever you are in your life’s journey. Take courses in Machine Learning, Robotics, Deep Language, Natural Language, Computer Vision, and more! #artificialintelligence #aicourses #learnai #A.I. #Artificial Intelligence #Artificial Intelligence Courses #online masters in ai and machine learning #artificial intelligence course #artificial intelligence programs #masters in artificial intelligence #masters in machine learning #artificial intelligence degree #learn ai programming #artificial intelligence masters programs #online masters in artificial intelligence
2020年07月30日
00:00:00 - 00:02:18
Algorithms and Data Structures Tutorial - Full Course for Beginners

Algorithms and Data Structures Tutorial - Full Course for Beginners

In this course you will learn about algorithms and data structures, two of the fundamental topics in computer science. There are three main parts to this course: algorithms, data structures, and a deep dive into sorting and searching algorithms. By the end, you will understand what algorithms and data structures are, how they are measured and evaluated, and how they are used to solve problems. This course was developed by Pasan Premaratne and Jay McGavren. It was made possible by a grant from teamtreehouse.com ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Introduction to Algorithms ⌨️ (1:57:44) Introduction to Data Structures ⌨️ (4:11:02) Algorithms: Sorting and Searching ⭐️ Code Snippets for Course ⭐️ 💻 Introduction to Algorithms: ⌨️ Algorithms in Code: 🔗 Linear Search Implementations: https://teamtreehouse.com/library/introduction-to-algorithms/algorithms-in-code/linear-search-implementations 🔗 Binary Search Implementations: https://teamtreehouse.com/library/introduction-to-algorithms/algorithms-in-code/binary-search-implementations 💻 Introduction to Data Structures ⌨️ Exploring Arrays: 🔗 Array Characteristics and Storage: https://teamtreehouse.com/library/introduction-to-data-structures/exploring-arrays/array-characteristics-and-storage 🔗 Operations on Arrays: https://teamtreehouse.com/library/introduction-to-data-structures/exploring-arrays/operations-on-arrays ⌨️ Building a Linked List: 🔗 Singly and Doubly Linked Lists: https://teamtreehouse.com/library/introduction-to-data-structures/building-a-linked-list/singly-and-doubly-linked-lists-2 🔗 Linked List Operations: https://teamtreehouse.com/library/introduction-to-data-structures/building-a-linked-list/linked-lists-operations ⌨️ The Merge Sort Algorithm: 🔗 Merge Sort Implementations: https://teamtreehouse.com/library/introduction-to-data-structures/the-merge-sort-algorithm/merge-sort-implementations 🔗 Alternate Versions of Merge Sort: https://teamtreehouse.com/library/introduction-to-data-structures/the-merge-sort-algorithm/alternate-versions-of-merge-sort ⌨️ Merge Sort and Linked Lists: 🔗 Implementing Merge Sort on Linked Lists: https://teamtreehouse.com/library/introduction-to-data-structures/merge-sort-and-linked-lists/implementing-merge-sort-on-linked-lists 💻 Algorithms: Sorting and Searching ⌨️ Sorting Algorithms: 🔗 Code for Bogosort: https://teamtreehouse.com/library/algorithms-sorting-and-searching/sorting-algorithms/code-for-bogosort 🔗 Code for Selection Sort: https://teamtreehouse.com/library/algorithms-sorting-and-searching/sorting-algorithms/code-for-selection-sort 🔗 Code for Quicksort: https://teamtreehouse.com/library/algorithms-sorting-and-searching/sorting-algorithms/code-for-quicksort 🔗 Code for Merge Sort: https://teamtreehouse.com/library/algorithms-sorting-and-searching/sorting-algorithms/code-for-merge-sort ⌨️ Searching Names: 🔗 Code for Linear Search: https://teamtreehouse.com/library/algorithms-sorting-and-searching/searching-names/code-for-linear-search 🔗 Code for Binary Search: https://teamtreehouse.com/library/algorithms-sorting-and-searching/searching-names/code-for-binary-search -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news
2021年03月19日
00:00:00 - 05:22:09
Data Science Full Course - Learn Data Science in 10 Hours | Data Science For Beginners | Edureka

Data Science Full Course - Learn Data Science in 10 Hours | Data Science For Beginners | Edureka

🔥 Data Science Course (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/masters-program/data-scientist-certification This Edureka Data Science Full Course video will help you understand and learn Data Science Algorithms in detail. This Data Science Tutorial is ideal for both beginners as well as professionals who want to master Data Science Algorithms. Below are the topics covered in this Data Science for Beginners course: 00:00 Data Science Full Course Agenda 2:44 Introduction to Data Science 9:55 Data Analysis at Walmart 13:20 What is Data Science? 14:39 Who is a Data Scientist? 16:50 Data Science Skill Set 21:51 Data Science Job Roles 26:58 Data Life Cycle 30:25 Statistics & Probability 34:31 Categories of Data 34:50 Qualitative Data 36:09 Quantitative Data 39:11 What is Statistics? 41:32 Basic Terminologies in Statistics 42:50 Sampling Techniques 45:31 Random Sampling 46:20 Systematic Sampling 46:50 Stratified Sampling 47:54 Types of Statistics 50:38 Descriptive Statistics 55:52 Measures of Spread 55:56 Range 56:44 Inter Quartile Range 58:58 Variance 59:36 Standard Deviation 1:14:25 Confusion Matrix 1:19:16 Probability 1:24:14 What is Probability? 1:27:13 Types of Events 1:27:58 Probability Distribution 1:28:15 Probability Density Function 1:30:02 Normal Distribution 1:30:51 Standard Deviation & Curve 1:31:19 Central Limit Theorem 1:33:12 Types of Probability 1:33:34 Marginal Probability 1:34:06 Joint Probability 1:34:58 Conditional Probability 1:35:56 Use-Case 1:39:46 Bayes Theorem 1:45:44 Inferential Statistics 1:56:40 Hypothesis Testing 2:00:34 Basics of Machine Learning 2:01:41 Need for Machine Learning 2:07:03 What is Machine Learning? 2:09:21 Machine Learning Definitions 2:11:48 Machine Learning Process 2:18:31 Supervised Learning Algorithm 2:19:54 What is Regression? 2:21:23 Linear vs Logistic Regression 2:33:51 Linear Regression 2:25:27 Where is Linear Regression used? 2:27:11 Understanding Linear Regression 2:37:00 What is R-Square? 2:46:35 Logistic Regression 2:51:22 Logistic Regression Curve 2:53:02 Logistic Regression Equation 2:56:21 Logistic Regression Use-Cases 2:58:23 Demo 3:00:57 Implement Logistic Regression 3:02:33 Import Libraries 3:05:28 Analyzing Data 3:11:52 Data Wrangling 3:23:54 Train & Test Data 3:20:44 Implement Logistic Regression 3:31:04 SUV Data Analysis 3:38:44 Decision Trees 3:39:50 What is Classification? 3:42:27 Types of Classification 3:42:27 Decision Tree 3:43:51 Random Forest 3:45:06 Naive Bayes 3:47:12 KNN 3:49:02 What is Decision Tree? 3:55:15 Decision Tree Terminologies 3:56:51 CART Algorithm 3:58:50 Entropy 4:00:15 What is Entropy? 4:23:52 Random Forest 4:27:29 Types of Classifier 4:31:17 Why Random Forest? 4:39:14 What is Random Forest? 4:51:26 How Random Forest Works? 4:51:36 Random Forest Algorithm 5:04:23 K Nearest Neighbour 5:05:33 What is KNN Algorithm? 5:08:50 KNN Algorithm Working 5:24:30 What is Naive Bayes? 5:25:13 Bayes Theorem 5:27:48 Bayes Theorem Proof 5:29:43 Naive Bayes Working 5:39:06 Types of Naive Bayes 5:53:37 Support Vector Machine 5:57:40 What is SVM? 5:59:46 How does SVM work? 6:03:00 Introduction to Non-Linear SVM 6:04:48 SVM Example 6:06:12 Unsupervised Learning Algorithms - KMeans 6:06:18 What is Unsupervised Learning? 6:06:45 Unsupervised Learning: Process Flow 6:07:17 What is Clustering? 6:09:15 Types of Clustering 6:10:15 K-Means Clustering 6:10:40 K-Means Algorithm Working 6:16:17 K-Means Algorithm 6:19:16 Fuzzy C-Means Clustering 6:21:22 Hierarchical Clustering 6:22:53 Association Clustering 6:24:57 Association Rule Mining 6:30:35 Apriori Algorithm 6:37:45 Apriori Demo 6:40:49 What is Reinforcement Learning? 6:42:48 Reinforcement Learning Process 6:51:10 Markov Decision Process 6:54:53 Understanding Q - Learning 7:13:12 Q-Learning Demo 7:25:34 The Bellman Equation 7:48:39 What is Deep Learning? 7:52:53 Why we need Artificial Neuron? 7:54:33 Perceptron Learning Algorithm 7:57:57 Activation Function 8:03:14 Single Layer Perceptron 8:04:04 What is Tensorflow? 8:07:25 Demo 8:21:03 What is a Computational Graph? 8:49:18 Limitations of Single Layer Perceptron 8:50:08 Multi-Layer Perceptron 8:51:24 What is Backpropagation? 8:52:26 Backpropagation Learning Algorithm 8:59:31 Multi-layer Perceptron Demo 9:01:23 Data Science Interview Questions 🔴 Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV Edureka Data Science Training & Certifications 🔵 Data Science Training using Python: http://bit.ly/2P2Qbl8 🔵 Python Programming Training: http://bit.ly/2OYsVoE 🔵 Python Masters Program: https://bit.ly/3e640cY 🔵 Machine Learning Course using Python: http://bit.ly/2SApG99 🔵 Data Scientist Masters Program: http://bit.ly/39HLiWJ 🔵 Machine Learning Engineer Masters Program: http://bit.ly/38Ch2MC For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: +18338555775 (toll free). #yt:cc=on #data science #Data Science full course #data science course #data science for beginners #data science course for beginners #data science tutorial #data science training #introduction to data science #data science tutorial for beginners #data scientist #what is data science #Learn Data Science #who is a data scientist #data science with python #statistics for data science #edureka #data science edureka #python edureka #machine learning edureka #edureka data science
2019年08月18日
00:00:00 - 10:23:57
Create a Large Language Model from Scratch with Python – Tutorial

Create a Large Language Model from Scratch with Python – Tutorial

Learn how to build your own large language model, from scratch. This course goes into the data handling, math, and transformers behind large language models. You will use Python. ✏️ Course developed by @elliotarledge 💻 Code and course resources: https://github.com/Infatoshi/fcc-intro-to-llms Join Elliot's Discord server: https://discord.gg/pV7ByF9VNm Elliot on X: https://twitter.com/elliotarledge ⭐️ Contents ⭐️ (0:00:00) Intro (0:03:25) Install Libraries (0:06:24) Pylzma build tools (0:08:58) Jupyter Notebook (0:12:11) Download wizard of oz (0:14:51) Experimenting with text file (0:17:58) Character-level tokenizer (0:19:44) Types of tokenizers (0:20:58) Tensors instead of Arrays (0:22:37) Linear Algebra heads up (0:23:29) Train and validation splits (0:25:30) Premise of Bigram Model (0:26:41) Inputs and Targets (0:29:29) Inputs and Targets Implementation (0:30:10) Batch size hyperparameter (0:32:13) Switching from CPU to CUDA (0:33:28) PyTorch Overview (0:42:49) CPU vs GPU performance in PyTorch (0:47:49) More PyTorch Functions (1:06:03) Embedding Vectors (1:11:33) Embedding Implementation (1:13:06) Dot Product and Matrix Multiplication (1:25:42) Matmul Implementation (1:26:56) Int vs Float (1:29:52) Recap and get_batch (1:35:07) nnModule subclass (1:37:05) Gradient Descent (1:50:53) Logits and Reshaping (1:59:28) Generate function and giving the model some context (2:03:58) Logits Dimensionality (2:05:17) Training loop + Optimizer + Zerograd explanation (2:13:56) Optimizers Overview (2:17:04) Applications of Optimizers (2:18:11) Loss reporting + Train VS Eval mode (2:32:54) Normalization Overview (2:35:45) ReLU, Sigmoid, Tanh Activations (2:45:15) Transformer and Self-Attention (2:46:55) Transformer Architecture (3:17:54) Building a GPT, not Transformer model (3:19:46) Self-Attention Deep Dive (3:25:05) GPT architecture (3:27:07) Switching to Macbook (3:31:42) Implementing Positional Encoding (3:36:57) GPTLanguageModel initalization (3:40:52) GPTLanguageModel forward pass (3:46:56) Standard Deviation for model parameters (4:00:50) Transformer Blocks (4:04:54) FeedForward network (4:07:53) Multi-head Attention (4:12:49) Dot product attention (4:19:43) Why we scale by 1/sqrt(dk) (4:26:45) Sequential VS ModuleList Processing (4:30:47) Overview Hyperparameters (4:32:14) Fixing errors, refining (4:34:01) Begin training (4:35:46) OpenWebText download and Survey of LLMs paper (4:37:56) How the dataloader/batch getter will have to change (4:41:20) Extract corpus with winrar (4:43:44) Python data extractor (4:49:23) Adjusting for train and val splits (4:57:55) Adding dataloader (4:59:04) Training on OpenWebText (5:02:22) Training works well, model loading/saving (5:04:18) Pickling (5:05:32) Fixing errors + GPU Memory in task manager (5:14:05) Command line argument parsing (5:18:11) Porting code to script (5:22:04) Prompt: Completion feature + more errors (5:24:23) nnModule inheritance + generation cropping (5:27:54) Pretraining vs Finetuning (5:33:07) R&D pointers (5:44:38) Outro 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news
2023年08月26日
00:00:00 - 05:43:41
Reinforcement Learning Course - Full Machine Learning Tutorial

Reinforcement Learning Course - Full Machine Learning Tutorial

Reinforcement learning is an area of machine learning that involves taking right action to maximize reward in a particular situation. In this full tutorial course, you will get a solid foundation in reinforcement learning core topics. The course covers Q learning, SARSA, double Q learning, deep Q learning, and policy gradient methods. These algorithms are employed in a number of environments from the open AI gym, including space invaders, breakout, and others. The deep learning portion uses Tensorflow and PyTorch. The course begins with more modern algorithms, such as deep q learning and policy gradient methods, and demonstrates the power of reinforcement learning. Then the course teaches some of the fundamental concepts that power all reinforcement learning algorithms. These are illustrated by coding up some algorithms that predate deep learning, but are still foundational to the cutting edge. These are studied in some of the more traditional environments from the OpenAI gym, like the cart pole problem. 💻Code: https://github.com/philtabor/Youtube-Code-Repository/tree/master/ReinforcementLearning ⭐️ Course Contents ⭐️ ⌨️ (00:00:00) Intro ⌨️ (00:01:30) Intro to Deep Q Learning ⌨️ (00:08:56) How to Code Deep Q Learning in Tensorflow ⌨️ (00:52:03) Deep Q Learning with Pytorch Part 1: The Q Network ⌨️ (01:06:21) Deep Q Learning with Pytorch part 2: Coding the Agent ⌨️ (01:28:54) Deep Q Learning with Pytorch part ⌨️ (01:46:39) Intro to Policy Gradients 3: Coding the main loop ⌨️ (01:55:01) How to Beat Lunar Lander with Policy Gradients ⌨️ (02:21:32) How to Beat Space Invaders with Policy Gradients ⌨️ (02:34:41) How to Create Your Own Reinforcement Learning Environment Part 1 ⌨️ (02:55:39) How to Create Your Own Reinforcement Learning Environment Part 2 ⌨️ (03:08:20) Fundamentals of Reinforcement Learning ⌨️ (03:17:09) Markov Decision Processes ⌨️ (03:23:02) The Explore Exploit Dilemma ⌨️ (03:29:19) Reinforcement Learning in the Open AI Gym: SARSA ⌨️ (03:39:56) Reinforcement Learning in the Open AI Gym: Double Q Learning ⌨️ (03:54:07) Conclusion Course from Machine Learning with Phil. Check out his YouTube channel: https://www.youtube.com/channel/UC58v9cLitc8VaCjrcKyAbrw -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org #reinforcement learning #machine learning #machine learning course #reinforcement learning course #deep q network #deep q-learning algorithm #deep learning game #deep q learning tutorial #q-learning #deep reinforcement learning #q learning #neural network #python #pytorch #python tutorial #deep learning #reinforcement learning python #artificial intelligence
2019年05月14日
00:00:00 - 03:55:27
Artificial Intelligence Full Course | Artificial Intelligence Tutorial for Beginners | Edureka

Artificial Intelligence Full Course | Artificial Intelligence Tutorial for Beginners | Edureka

🔥 Machine Learning Engineer Masters Program (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/masters-program/machine-learning-engineer-training This Edureka video on "Artificial Intelligence Full Course" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples. The following topics are covered in this Artificial Intelligence Full Course: 00:00 Introduction to Artificial Intelligence Course 02:27 History Of AI 06:45 Demand For AI 08:46 What Is Artificial Intelligence? 09:50 AI Applications 16:49 Types Of AI 20:24 Programming Languages For AI 27:12 Introduction To Machine Learning 28:08 Need For Machine Learning 31:48 What Is Machine Learning? 34:13 Machine Learning Definitions 37:26 Machine Learning Process 49:13 Types Of Machine Learning 49:21 Supervised Learning 52:00 Unsupervised Learning 53:44 Reinforcement Learning 55:29 Supervised vs Unsupervised vs Reinforcement Learning 58:23 Types Of Problems Solved Using Machine Learning 1:04:49 Supervised Learning Algorithms 1:05:17 Linear Regression 1:11:20 Linear Regression Demo 1:26:36 Logistic Regression 1:35:36 Decision Tree 1:55:18 Random Forest 2:07:31 Naive Bayes 2:14:37 K Nearest Neighbour (KNN) 2:20:31 Support Vector Machine (SVM) 2:26:40 Demo (Classification Algorithms) 2:42:36 Unsupervised Learning Algorithms 2:42:45 K-means Clustering 2:50:49 Demo (Unsupervised Learning) 2:56:40 Reinforcement Learning 3:24:36 Demo (Reinforcement Learning) 3:31:41 AI vs Machine Learning vs Deep Learning 3:33:08 Limitations Of Machine Learning 3:36:32 Introduction To Deep Learning 3:38:36 How Deep Learning Works? 3:40:48 What Is Deep Learning? 3:41:50 Deep Learning Use Case 3:43:14 Single Layer Perceptron 3:50:56 Multi Layer Perceptron (ANN) 3:52:55 Backpropagation 3:54:39 Training A Neural Network 4:01:02 Limitations Of Feed Forward Network 4:03:18 Recurrent Neural Networks 4:05:36 Convolutional Neural Networks 4:09:00 Demo (Deep Learning) 4:29:02 Natural Language Processing 4:30:53 What Is Text Mining? 4:32:43 What Is NLP? 4:33:26 Applications Of NLP 4:35:53 Terminologies In NLP 4:41:19 NLP Demo 4:47:21 Machine Learning Masters Program 🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py Python Full Course: https://www.youtube.com/watch?v=WGJJIrtnfpk Statistics and Probability Tutorial: https://www.youtube.com/watch?v=XcLO4f1i4Yo 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝐲𝐭𝐡𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠𝐬 🔵 Python Programming Certification: http://bit.ly/37rEsnA 🔵 Python Certification Training for Data Science: http://bit.ly/2Gj6fux 🔴. 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 🔵 Data Scientist Masters Program: http://bit.ly/2t1snGM 🔵 Machine Learning Engineer Masters Program: https://bit.ly/3Hi1sXN 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh 🔵 University of Cambridge Online Certifications: https://bit.ly/3RSNTXi 📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 𝟐𝟎𝟐𝟒 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢 ⏩ NEW Top 10 Technologies To Learn In 2024 - https://www.youtube.com/watch?v=vaLXPv0ewHU Check out the entire Machine Learning Playlist: https://bit.ly/2NG9tK4 Check out the entire Machine Learning Blog list: https://bit.ly/2V2MnDW #edureka #artificialIntelligence #artificialIntelligenceTutorial #artificialIntelligenceFullCourse #artificialIntelligenceEngineer 📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates 📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain 📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka 📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: https://www.instagram.com/edureka_learning/ 📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/ 📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN 📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN 📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/ 📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/ ------------------------------------- About the Masters Program Edureka’s Machine Learning Engineer Masters Program makes you proficient in techniques like Supervised Learning, Unsupervised Learning and Natural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning. The Master's Program Covers Topics LIke: Python Programming PySpark HDFS Spark SQL Machine Learning Techniques and Artificial Intelligence Types Tokenization Named Entity Recognition Lemmatization Supervised Algorithms Unsupervised Algorithms Tensor Flow Deep learning Keras Neural Networks Bayesian and Markov’s Models Inference Decision Making Bandit Algorithms Bellman Equation Policy Gradient Methods. ------------- Please write back to us at [email protected] or call us at IND: 9606058406 / US: +18338555775 (toll-free) for more information #yt:cc=on #Artificial Intelligence Full Course #artificial intelligence #Artificial Intelligence Tutorial for Beginners #Artificial Intelligence Tutorial #Artificial Intelligence course #artificial intelligence crash course #artificial intelligence course for beginners #artificial intelligence training #edureka #what is artificial intelligence #artificial intelligence edureka #artificial intelligence for beginners #ai tutorial #Ai tutorial for beginners
2019年06月02日
00:00:00 - 04:52:51
Intermediate Python Programming Course

Intermediate Python Programming Course

Take your Python skills to the next level with this intermediate Python course. First, you will get a review of basic concepts such as lists, strings, and dictionaries, but with an emphasis on some lesser known capabilities. Then, you will learn more advanced topics such as threading, multiprocessing, context managers, generators, and more. 💻 Code: https://github.com/python-engineer/python-engineer-notebooks/tree/master/advanced-python 🎥 Course from Patrick Loeber. Check out his channel: https://www.youtube.com/channel/UCbXgNpp0jedKWcQiULLbDTA 🔗 Written Tutorials from Patrick: https://www.python-engineer.com/courses/advancedpython/ ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Intro ⌨️ (0:00:56) Lists ⌨️ (0:16:30) Tuples ⌨️ (0:29:49) Dictionaries ⌨️ (0:42:40) Sets ⌨️ (0:58:44) Strings ⌨️ (1:22:50) Collections ⌨️ (1:36:43) Itertools ⌨️ (1:51:50) Lambda Functions ⌨️ (2:04:03) Exceptions and Errors ⌨️ (2:20:10) Logging ⌨️ (2:42:20) JSON ⌨️ (2:59:42) Random Numbers ⌨️ (3:14:23) Decorators ⌨️ (3:35:32) Generators ⌨️ (3:53:29) Threading vs Multiprocessing ⌨️ (4:07:59) Multithreading ⌨️ (4:31:05) Multiprocessing ⌨️ (4:53:26) Function Arguments ⌨️ (5:17:28) The Asterisk (*) Operator ⌨️ (5:30:19) Shallow vs Deep Copying ⌨️ (5:40:07) Context Managers -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news #python course #python tutorial #python #intermediate python #python nprogramming
2020年08月28日
00:00:00 - 05:55:47
Data Analysis with Python - Full Course for Beginners (Numpy, Pandas, Matplotlib, Seaborn)

Data Analysis with Python - Full Course for Beginners (Numpy, Pandas, Matplotlib, Seaborn)

Learn Data Analysis with Python in this comprehensive tutorial for beginners, with exercises included! NOTE: Check description for updated Notebook links. Data Analysis has been around for a long time, but up until a few years ago, it was practiced using closed, expensive and limited tools like Excel or Tableau. Python, SQL and other open libraries have changed Data Analysis forever. In this tutorial you'll learn the whole process of Data Analysis: reading data from multiple sources (CSVs, SQL, Excel, etc), processing them using NumPy and Pandas, visualize them using Matplotlib and Seaborn and clean and process it to create reports. Additionally, we've included a thorough Jupyter Notebook tutorial, and a quick Python reference to refresh your programming skills. 💻 Course created by Santiago Basulto from DataWars 🔗 Check out all Data Science courses from DataWars: https://datawars.io/ref=fcc ⚠️ Note: Instead of loading the notebooks on notebooks.ai, you should use Google Colab instead. Here are instructions on loading a notebook directly from GitHub into Google Colab: https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb#scrollTo=K-NVg7RjyeTk  ⭐️ Course Contents ⭐️ ⌨️ Part 1: Introduction What is Data Analysis, why Python?, what other options are there? what's the cycle of a Data Analysis project? What's the difference between Data Analysis and Data Science? 🔗 Slides for this section: https://docs.google.com/presentation/d/1XXhVx2a7z2GrG5qddIyLFk4T_5s5mmdqSptDGBD9hWk/edit?usp=sharing ⌨️ Part 2: Real Life Example of a Python/Pandas Data Analysis project (00:11:11) A demonstration of a real life data analysis project using Python, Pandas, SQL and Seaborn. Don't worry, we'll dig deeper in the following sections 🔗 Notebooks: https://github.com/rmotr-curriculum/FreeCodeCamp-Pandas-Real-Life-Example ⌨️ Part 3: Jupyter Notebooks Tutorial (00:30:50) A step by step tutorial to learn how to use Juptyer Notebooks 🔗 Twitter Cheat Sheet: https://twitter.com/rmotr_com/status/1122176794696847361 🔗 Notebooks: https://github.com/rmotr-curriculum/ds-content-interactive-jupyterlab-tutorial ⌨️ Part 4: Intro to NumPy (01:04:58) Learn why NumPy was such an important library for the data-processing world in Python. Learn about low level details of computations and memory storage, and why tools like Excel will always be limited when processing large volumes of data. 🔗 Notebooks: https://github.com/rmotr-curriculum/freecodecamp-intro-to-numpy ⌨️ Part 5: Intro to Pandas (01:57:08) Pandas is arguably the most important library for Data Processing in the Python world. Learn how it works and how its main data structure, the Data Frame, compares to other tools like spreadsheets or DFs used for Big Data 🔗 Notebooks: https://github.com/rmotr-curriculum/freecodecamp-intro-to-pandas ⌨️ Part 6: Data Cleaning (02:47:18) Learn the different types of issues that we'll face with our data: null values, invalid values, statistical outliers, etc, and how to clean them. 🔗 Notebooks: https://github.com/rmotr-curriculum/data-cleaning-rmotr-freecodecamp ⌨️ Part 7: Reading Data from other sources (03:25:15) 🔗 Notebooks: https://github.com/rmotr-curriculum/RDP-Reading-Data-with-Python-and-Pandas ⌨️ Part 8: Python Recap (03:55:19) If your Python or coding skills are rusty, check out this section for a quick recap of Python main features and control flow structures. 🔗 Notebooks: https://github.com/rmotr-curriculum/ds-content-python-under-10-minutes -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news
2020年04月15日
00:00:00 - 04:22:13
Python API Development - Comprehensive Course for Beginners

Python API Development - Comprehensive Course for Beginners

Learn Python API development in one of the most comprehensive courses ever on the topic. You will build a full-fledged API in Python using FastAPI. You will learn the fundamentals of API design including routes, serialization/deserialization, schema validation, and models. You will also learn about SQL, testing with pytest, and how to build out a CI/CD pipeline using GitHub actions. 💻 Code: https://github.com/Sanjeev-Thiyagarajan/fastapi-course ✏️ Course from Sanjeev Thiyagarajan. Check out his channel: https://www.youtube.com/channel/UC2sYgV-NV6S5_-pqLGChoNQ ⭐️ Course Contents 00:00 Intro 06:33 Project Overview 11:22 Mac Python Installation 13:15 Mac VS Code install and setup 16:37 Windows Python Installation 18:30 Windows VS Code install and setup 22:11 Python virtual Env Basics 24:35 Virtual Env on windows 28:56 Virtual Env on Mac 34:17 Install dependencies w/ pip 36:21 Starting FastAPI 39:23 Path Operations 53:22 Intro toman 57:34 HTTP Requests 1:07:29 Schema Validation with Pydantic 1:22:45 CRUD Operations 1:29:44 Storing in Array 1:34:06 Creating 1:38:15 Postman Collections & saving requests 1:39:47 Retrieve One 1:48:10 Path order Matters 1:52:46 Changing response Status Codes 2:01:49 Deleting 2:10:31 Updating 2:18:02 Automatic Documentation 2:21:34 Python packages 2:24:11 Database Intro 2:28:54 Postgres Windows Install 2:31:28 Postgres Mac Install 2:34:26 Database Schema & Tables 2:44:35 Managing Postgres with PgAdmin GUI 3:12:10 Your first SQL Query 3:19:43 Filter results with "where" 3:22:55 SQL Operators 3:26:38 IN 3:28:07 Pattern matching with LIKE 3:31:59 Ordering Results 3:36:27 LIMIT & OFFSET 3:39:21 Modifying Data 3:53:48 Setup App Database 3:58:21 Connecting to database w/ Python 4:08:00 Database CRUD 4:31:18 ORM intro 4:35:33 SQLALCHEMY setup 4:55:25 Adding CreatedAt Column 5:00:59 Get All 5:07:55 Create 5:15:50 Get by ID 5:19:50 Delete 5:22:31 Update 5:28:21 Pydantic vs ORM Models 5:32:21 Pydantic Models Deep Dive 5:38:57 Response Model 5:50:08 Creating Users Table 5:54:50 User Registration Path Operation 6:03:27 Hashing Passwords 6:08:49 Refractor Hashing Logic 6:10:32 Get User by ID 6:17:13 FastAPI Routers 6:27:34 Router Prefix 6:30:31 Router Tags 6:32:49 JWT Token Basics 6:47:03 Login Process 7:00:44 Creating Token 7:09:58 OAuth2 PasswordRequestForm 7:13:23 Verify user is Logged In 7:25:21 Fixing Bugs 7:27:59 Protecting Routes 7:36:17 Test Expired Token 7:38:13 Fetching User in Protected Routes 7:42:44 Postman advanced Features 7:50:33 SQL Relationship Basics 7:54:59 Postgres Foreign Keys 8:07:20 SQLAlchemy Foreign Keys 8:13:40 Update Schema to include User 8:17:59 Assigning Owner id when creating new 8:21:01 Delete and Update only your own 8:27:48 Only Retrieving Logged in User's 8:33:37 Sqlalchemy Relationships 8:38:32 Query Parameters 8:50:46 Cleanup our main.py file 8:53:53 Env Variables 9:21:20 Vote/Like Theory 9:26:36 Votes Table 9:31:33 Votes Sqlalchemy 9:34:11 Votes Route 9:52:31 SQL Joins 10:15:26 Joins in SqlAlchemy 10:28:21 Get One with Joins 10:30:18 What is a database migration tool 10:33:45 Alembic Setup 11:13:50 Disable SqlAlchemy create Engine 11:14:28 What is CORS? 11:23:38 Git PreReqs 11:27:40 Git Install 11:29:23 Github 11:34:39 Heroku intro 11:35:40 Create Heroku App 11:40:21 Heroku procfile 11:44:59 Adding a Postgres database 11:48:42 Env Variables in Heroku 11:58:59 Alembic migrations on Heroku Postgres instance 12:02:52 Pushing changed to production 12:05:04 Create an Ubuntu VM 12:08:04 Update packages 12:10:47 Install Python 12:12:21 Install Postgres & setup password 12:17:28 Postgres Config 12:24:50 Create new user and setup python evironment 12:34:06 Env Variables 12:42:24 Alembic migrations on production database 12:45:57 Gunicorn 12:54:12 Creating a Systemd service 13:04:45 NGINX 13:10:45 Setting up Domain name 13:15:19 SSL/HTTPS 13:19:31 NGINX enable 13:20:06 Firewall 13:23:47 Pushing code changes to Production 13:26:09 Dockerfile 13:38:39 Docker Compose 13:48:34 Postgres Container 13:56:22 Bind Mounts 14:03:39 Dockerhub 14:08:08 Production vs Development 14:14:51 Testing Intro 14:17:19 Writing your first test 14:30:22 The -s & -v flags 14:31:44 Testing more functions 14:35:29 Parametrize 14:40:21 Testing Classes 14:48:37 Fixtures 14:55:40 Combining Fixtures + Parametrize 14:59:13 Testing Exceptions 15:06:07 FastAPI TestClient 15:14:26 Pytest flags 15:17:31 Test create user 15:25:23 Setup testing database 15:36:47 Create & destroy database after each test 15:44:18 More Fixtures to handle database interaction 15:50:35 Trailing slashes in path 15:53:12 Fixture scope 16:07:50 Test user fixture 16:14:40 Test/validate token 16:18:59 Conftest.py 16:22:09 Testing 17:34:15 CI/CD intro 17:43:29 Github Actions 17:49:32 Creating Jobs 17:57:38 setup python/dependencies/pytest 18:06:14 Env variables 18:11:19 Github Secrets 18:18:14 Testing database 18:23:42 Building Docker images 18:34:33 Deploy to heroku 18:49:10 Failing tests in pipeline 18:52:18 Deploy to Ubuntu
2021年11月01日
00:00:00 - 19:00:27
Object Oriented Programming with Python - Full Course for Beginners

Object Oriented Programming with Python - Full Course for Beginners

Object Oriented Programming is an important concept in software development. In this complete tutorial, you will learn all about OOP and how to implement it using Python. 💻 Code: https://github.com/jimdevops19/PythonOOP 🔗 Tutorial referenced for a deeper explanation about __repr__: https://www.youtube.com/watch?v=FIaPZXaePhw ✏️ Course developed by Jim from JimShapedCoding. Check out his channel: https://www.youtube.com/channel/UCU8d7rcShA7MGuDyYH1aWGg ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Getting Started with Classes ⌨️ (0:12:11) Constructor, __init__ ⌨️ (0:50:35) Class vs Static Methods ⌨️ (1:13:22) Inheritance ⌨️ (1:30:14) Getters and Setters ⌨️ (1:51:00) OOP Principles 🎉 Thanks to our Champion and Sponsor supporters: 👾 Wong Voon jinq 👾 hexploitation 👾 Katia Moran 👾 BlckPhantom 👾 Nick Raker 👾 Otis Morgan 👾 DeezMaster 👾 AppWrite -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news
2021年10月13日
00:00:00 - 02:12:35
Neural Networks from Scratch - P.1 Intro and Neuron Code

Neural Networks from Scratch - P.1 Intro and Neuron Code

Building neural networks from scratch in Python introduction. Neural Networks from Scratch book: https://nnfs.io Playlist for this series: https://www.youtube.com/playlist?list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3 Python 3 basics: https://pythonprogramming.net/introduction-learn-python-3-tutorials/ Intermediate Python (w/ OOP): https://pythonprogramming.net/introduction-intermediate-python-tutorial/ Mug link for fellow mug aficionados: https://amzn.to/2xcyfPC Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex #nnfs #python #neuralnetworks #neural networks #deep learning #python #nnfs #machine learning
2020年04月11日
00:00:00 - 00:16:59
Stanford CS25: V2 I Introduction to Transformers w/ Andrej Karpathy

Stanford CS25: V2 I Introduction to Transformers w/ Andrej Karpathy

January 10, 2023 Introduction to Transformers Andrej Karpathy: https://karpathy.ai/ Since their introduction in 2017, transformers have revolutionized Natural Language Processing (NLP). Now, transformers are finding applications all over Deep Learning, be it computer vision (CV), reinforcement learning (RL), Generative Adversarial Networks (GANs), Speech or even Biology. Among other things, transformers have enabled the creation of powerful language models like GPT-3 and were instrumental in DeepMind's recent AlphaFold2, that tackles protein folding. In this speaker series, we examine the details of how transformers work, and dive deep into the different kinds of transformers and how they're applied in different fields. We do this by inviting people at the forefront of transformers research across different domains for guest lectures. More about the course can be found here: https://web.stanford.edu/class/cs25/ View the entire CS25 Transformers United playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM 0:00 Introduction 0:47 Introducing the Course 3:19 Basics of Transformers 3:35 The Attention Timeline 5:01 Prehistoric Era 6:10 Where we were in 2021 7:30 The Future 10:15 Transformers - Andrej Karpathy 10:39 Historical context 1:00:30 Thank you - Go forth and transform #Stanford #Stanford Online
2023年05月20日
00:00:00 - 01:11:41
Python + JavaScript - Full Stack App Tutorial

Python + JavaScript - Full Stack App Tutorial

Dive deep into full stack development in this comprehensive guide, where I will walk you through building a dynamic web application from scratch, utilizing the power of Python with Flask for the backend and JavaScript with React for the frontend. Don't forget to like, share, and subscribe for more in-depth tech tutorials from Tech with Tim. Join the discussion in the comments below and share your progress or ask questions. Register for the NVIDIA GTC: https://nvda.ws/3j19YDK Enter the Giveaway: https://forms.gle/Lu7QQ4XancakaTjH8 The Future of Extended Reality and Generative AI [S62751]: https://www.nvidia.com/gtc/session-catalog/?tab.allsessions=1700692987788001F1cG&search.parvrp=1699042406066003SCBr#/session/1697836797138001TTNw?ncid=ref-inpa-737311 Large-Scale Production Deployment of RAG Pipelines [DLIT63054] https://www.nvidia.com/gtc/session-catalog/?tab.allsessions=1700692987788001F1cG&search.pconversationalainlpp=1699468109960003YuO1#/session/1702668294942001QSt8?ncid=ref-inpa-737311 Exploring AI-Assisted Developer Tools for Accelerated Computing [SE62128] https://www.nvidia.com/gtc/session-catalog/?tab.allsessions=1700692987788001F1cG&search.pgenerativeaip=1699468419333006hhuM#/session/1694625432057001G65M?ncid=ref-inpa-737311 🎞 Video Resources 🎞 Full Code: https://github.com/techwithtim/Flask-React-Full-Stack-App If you want to land a developer job: https://techwithtim.net/dev If you want to learn Web 3 & Blockchain: https://algoexpert.io/blockchain (use code “tim”) If you want learn Python & Go: https://programmingexpert.io/tim (use code “tim”) Skool community for free resources: https://softwaredeveloperacademy.com Discord server: https://discord.gg/twt ⏳ Timestamps ⏳ 00:00 | Introduction 02:40 | Project Demo 04:27 | Setup & Installation 08:50 | Building The Backend 45:08 | Building The Frontend Hashtags #PythonJavaScriptTutorial #fullstackdevelopment #FlaskReact #TechWithTim #WebDevelopmentGuide #tech with tim #Python Flask Tutorial #JavaScript React Guide #Full Stack Development #Tech with Tim #Web Application Tutorial #Flask React Integration #Python JavaScript Projects #Learn Full Stack #Flask Web Development #React for Beginners #Advanced Programming #Coding Tutorial #Web Dev Skills #Full Stack Python JS #Building Web Apps #Flask React Tutorial #Full Stack Course #Python Web Development #JavaScript Frameworks #Tech Tutorials
2024年02月20日
00:00:00 - 01:29:25
Deep Learning for Computer Vision with Python and TensorFlow – Complete Course

Deep Learning for Computer Vision with Python and TensorFlow – Complete Course

Learn the basics of computer vision with deep learning and how to implement the algorithms using Tensorflow. Author: Folefac Martins from Neuralearn.ai More Courses: www.neuralearn.ai Link to Code: https://colab.research.google.com/drive/18u1KDx-9683iZNPxSDZ6dOv9319ZuEC_ YouTube Channel: https://www.youtube.com/@neuralearn ⭐️ Contents ⭐️ Introduction ⌨️ (0:00:00) Welcome ⌨️ (0:05:54) Prerequisite ⌨️ (0:06:11) What we shall Learn Tensors and Variables ⌨️ (0:12:12) Basics ⌨️ (0:19:26) Initialization and Casting ⌨️ (1:07:31) Indexing ⌨️ (1:16:15) Maths Operations ⌨️ (1:55:02) Linear Algebra Operations ⌨️ (2:56:21) Common TensorFlow Functions ⌨️ (3:50:15) Ragged Tensors ⌨️ (4:01:41) Sparse Tensors ⌨️ (4:04:23) String Tensors ⌨️ (4:07:45) Variables Building Neural Networks with TensorFlow [Car Price Prediction] ⌨️ (4:14:52) Task Understanding ⌨️ (4:19:47) Data Preparation ⌨️ (4:54:47) Linear Regression Model ⌨️ (5:10:18) Error Sanctioning ⌨️ (5:24:53) Training and Optimization ⌨️ (5:41:22) Performance Measurement ⌨️ (5:44:18) Validation and Testing ⌨️ (6:04:30) Corrective Measures Building Convolutional Neural Networks with TensorFlow [Malaria Diagnosis] ⌨️ (6:28:50) Task Understanding ⌨️ (6:37:40) Data Preparation ⌨️ (6:57:40) Data Visualization ⌨️ (7:00:20) Data Processing ⌨️ (7:08:50) How and Why ConvNets Work ⌨️ (7:56:15) Building Convnets with TensorFlow ⌨️ (8:02:39) Binary Crossentropy Loss ⌨️ (8:10:15) Training Convnets ⌨️ (8:23:33) Model Evaluation and Testing ⌨️ (8:29:15) Loading and Saving Models to Google Drive Building More Advanced Models in Teno Convolutional Neural Networks with TensorFlow [Malaria Diagnosis] ⌨️ (8:47:10) Functional API ⌨️ (9:03:48) Model Subclassing ⌨️ (9:19:05) Custom Layers Evaluating Classification Models [Malaria Diagnosis] ⌨️ (9:36:45) Precision, Recall and Accuracy ⌨️ (10:00:35) Confusion Matrix ⌨️ (10:10:10) ROC Plots Improving Model Performance [Malaria Diagnosis] ⌨️ (10:18:10) TensorFlow Callbacks ⌨️ (10:43:55) Learning Rate Scheduling ⌨️ (11:01:25) Model Checkpointing ⌨️ (11:09:25) Mitigating Overfitting and Underfitting Data Augmentation [Malaria Diagnosis] ⌨️ (11:38:50) Augmentation with tf.image and Keras Layers ⌨️ (12:38:00) Mixup Augmentation ⌨️ (12:56:35) Cutmix Augmentation ⌨️ (13:38:30) Data Augmentation with Albumentations Advanced TensorFlow Topics [Malaria Diagnosis] ⌨️ (13:58:35) Custom Loss and Metrics ⌨️ (14:18:30) Eager and Graph Modes ⌨️ (14:31:23) Custom Training Loops Tensorboard Integration [Malaria Diagnosis] ⌨️ (14:57:00) Data Logging ⌨️ (15:29:00) View Model Graphs ⌨️ (15:31:45) Hyperparameter Tuning ⌨️ (15:52:40) Profiling and Visualizations MLOps with Weights and Biases [Malaria Diagnosis] ⌨️ (16:00:35) Experiment Tracking ⌨️ (16:55:02) Hyperparameter Tuning ⌨️ (17:17:15) Dataset Versioning ⌨️ (18:00:23) Model Versioning Human Emotions Detection ⌨️ (18:16:55) Data Preparation ⌨️ (18:45:38) Modeling and Training ⌨️ (19:36:42) Data Augmentation ⌨️ (19:54:30) TensorFlow Records Modern Convolutional Neural Networks [Human Emotions Detection] ⌨️ (20:31:25) AlexNet ⌨️ (20:48:35) VGGNet ⌨️ (20:59:50) ResNet ⌨️ (21:34:07) Coding ResNet from Scratch ⌨️ (21:56:17) MobileNet ⌨️ (22:20:43) EfficientNet Transfer Learning [Human Emotions Detection] ⌨️ (22:38:15) Feature Extraction ⌨️ (23:02:25) Finetuning Understanding the Blackbox [Human Emotions Detection] ⌨️ (23:15:33) Visualizing Intermediate Layers ⌨️ (23:36:20) Gradcam method Transformers in Vision [Human Emotions Detection] ⌨️ (23:57:35) Understanding ViTs ⌨️ (24:51:17) Building ViTs from Scratch ⌨️ (25:42:39) FineTuning Huggingface ViT ⌨️ (26:05:52) Model Evaluation with Wandb Model Deployment [Human Emotions Detection] ⌨️ (26:27:13) Converting TensorFlow Model to Onnx format ⌨️ (26:52:26) Understanding Quantization ⌨️ (27:13:08) Practical Quantization of Onnx Model ⌨️ (27:22:01) Quantization Aware Training ⌨️ (27:39:55) Conversion to TensorFlow Lite ⌨️ (27:58:28) How APIs work ⌨️ (28:18:28) Building an API with FastAPI ⌨️ (29:39:10) Deploying API to the Cloud ⌨️ (29:51:35) Load Testing with Locust Object Detection with YOLO ⌨️ (30:05:29) Introduction to Object Detection ⌨️ (30:11:39) Understanding YOLO Algorithm ⌨️ (31:15:17) Dataset Preparation ⌨️ (31:58:27) YOLO Loss ⌨️ (33:02:58) Data Augmentation ⌨️ (33:27:33) Testing Image Generation ⌨️ (33:59:28) Introduction to Image Generation ⌨️ (34:03:18) Understanding Variational Autoencoders ⌨️ (34:20:46) VAE Training and Digit Generation ⌨️ (35:06:05) Latent Space Visualization ⌨️ (35:21:36) How GANs work ⌨️ (35:43:30) The GAN Loss ⌨️ (36:01:38) Improving GAN Training ⌨️ (36:25:02) Face Generation with GANs Conclusion ⌨️ (37:15:45) What's Next
2023年06月06日
00:00:00 - 37:16:41
Database Design Course - Learn how to design and plan a database for beginners

Database Design Course - Learn how to design and plan a database for beginners

This database design course will help you understand database concepts and give you a deeper grasp of database design. Database design is the organisation of data according to a database model. The designer determines what data must be stored and how the data elements interrelate. With this information, they can begin to fit the data to the database model. Learn more about this course on Caleb Curry's website: https://www.calebcurry.com/freecodecamp-database-design-full-course/ ⭐️ Contents ⭐ ⌨️ (0:00:00) Introduction ⌨️ (0:03:12) What is a Database? ⌨️ (0:11:04) What is a Relational Database? ⌨️ (0:23:42) RDBMS ⌨️ (0:37:32) Introduction to SQL ⌨️ (0:44:01) Naming Conventions ⌨️ (0:47:16) What is Database Design? ⌨️ (1:00:26) Data Integrity ⌨️ (1:13:28) Database Terms ⌨️ (1:28:28) More Database Terms ⌨️ (1:38:46) Atomic Values ⌨️ (1:44:25) Relationships ⌨️ (1:50:35) One-to-One Relationships ⌨️ (1:53:45) One-to-Many Relationships ⌨️ (1:57:50) Many-to-Many Relationships ⌨️ (2:02:24) Designing One-to-One Relationships ⌨️ (2:13:40) Designing One-to-Many Relationships ⌨️ (2:23:50) Parent Tables and Child Tables ⌨️ (2:30:42) Designing Many-to-Many Relationships ⌨️ (2:46:23) Summary of Relationships ⌨️ (2:54:42) Introduction to Keys ⌨️ (3:07:24) Primary Key Index ⌨️ (3:13:42) Look up Table ⌨️ (3:30:19) Superkey and Candidate Key ⌨️ (3:48:59) Primary Key and Alternate Key ⌨️ (3:56:34) Surrogate Key and Natural Key ⌨️ (4:03:43) Should I use Surrogate Keys or Natural Keys? ⌨️ (4:13:07) Foreign Key ⌨️ (4:25:15) NOT NULL Foreign Key ⌨️ (4:38:17) Foreign Key Constraints ⌨️ (4:49:50) Simple Key, Composite Key, Compound Key ⌨️ (5:01:54) Review and Key Points....HA GET IT? KEY points! ⌨️ (5:10:28) Introduction to Entity Relationship Modeling ⌨️ (5:17:34) Cardinality ⌨️ (5:24:41) Modality ⌨️ (5:35:14) Introduction to Database Normalization ⌨️ (5:39:48) 1NF (First Normal Form of Database Normalization) ⌨️ (5:46:34) 2NF (Second Normal Form of Database Normalization) ⌨️ (5:55:00) 3NF (Third Normal Form of Database Normalization) ⌨️ (6:01:12) Indexes (Clustered, Nonclustered, Composite Index) ⌨️ (6:14:36) Data Types ⌨️ (6:25:55) Introduction to Joins ⌨️ (6:39:23) Inner Join ⌨️ (6:54:48) Inner Join on 3 Tables ⌨️ (7:07:41) Inner Join on 3 Tables (Example) ⌨️ (7:23:53) Introduction to Outer Joins ⌨️ (7:29:46) Right Outer Join ⌨️ (7:35:33) JOIN with NOT NULL Columns ⌨️ (7:42:40) Outer Join Across 3 Tables ⌨️ (7:48:24) Alias ⌨️ (7:52:13) Self Join 🎥Course developed by Caleb Curry. Check out his YouTube channel: https://www.youtube.com/user/CalebTheVideoMaker2 🐦Follow Caleb on Twitter: https://twitter.com/calebcurry -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org #Database Design Tutorial #Database Design #Relational Database Design #Database Design Overview #Database Normalization #database design course #database design tutorial #database design for beginners #er models #relational database #relational database design tutorial #database #caleb curry #freecodecamp #SQL Server Design Overview #join #inner join #database index #database key #sql #sql tutorial
2018年09月01日
00:00:00 - 08:07:20
Azure AI Fundamentals Certification 2024 (AI-900) - Full Course to PASS the Exam

Azure AI Fundamentals Certification 2024 (AI-900) - Full Course to PASS the Exam

Prepare for the Azure AI Fundamentals Certification (AI-900) and pass! This course has been updated for 2024. ✏️ Course developed by Andrew Brown of ExamPro. @ExamProChannel 🔗 ExamPro Cloud Obsessed Certification Training: https://www.exampro.co ⭐️ Contents ⭐️ ☁️ Introduction 🎤 (00:00:00) Introduction to AI-900 🎤 (00:08:18) Exam Guide Breakdown ☁️ ML Introduction 🎤 (00:12:51) Layers of Machine Learning 🎤 (00:13:59) Key Elements of AI 🎤 (00:14:57) DataSets 🎤 (00:16:37) Labeling 🎤 (00:17:43) Supervised and Unsupervised Reinforcement 🎤 (00:19:09) Netural Networks and Deep Learning 🎤 (00:21:25) GPU 🎤 (00:22:21) CUDA 🎤 (00:23:29) Simple ML Pipeline 🎤 (00:25:39) Forecast vs Prediction 🎤 (00:26:24) Metrics 🎤 (00:27:58) Juypter Notebooks 🎤 (00:29:13) Regression 🎤 (00:30:50) Classification 🎤 (00:31:44) Clustering 🎤 (00:32:29) Confusion Matrix ☁️ Common AI Workloads 🎤 (00:34:06) Anomaly Detection AI 🎤 (00:34:59) Computer Vision AI 🎤 (00:37:05) Natural Language Processing AI 🎤 (00:38:42) Conversational AI ☁️ Responsible AI 🎤 (00:40:16) Responsible AI 🎤 (00:41:09) Fairness 🎤 (00:42:08) Reliability and safety 🎤 (00:43:00) Privacy and security 🎤 (00:43:45) Inclusiveness 🎤 (00:44:24) Transparency 🎤 (00:45:00) Accountability 🎤 (00:45:45) Guidelines for Human AI Interaction 🎤 (00:46:04) Follow Along Guidelines for Human AI Interaction ☁️ Congitive Services 🎤 (00:57:33) Azure Cognitive Services 🎤 (00:59:41) Congitive API Key and Endpoint 🎤 (01:00:08) Knowledge Mining 🎤 (01:04:42) Face Service 🎤 (01:06:30) Speech and Translate Service 🎤 (01:08:04) Text Analytics 🎤 (01:11:02) OCR Computer Vision 🎤 (01:12:22) Form Recognizer 🎤 (01:14:48) Form Recognizer Custom Models 🎤 (01:15:34) Form Recognizer Prebuilt Models 🎤 (01:17:33) LUIS 🎤 (01:19:58) QnA Maker 🎤 (01:24:19) Azure Bot Service ☁️ ML Studio 🎤 (01:26:45) Azure Machine Learning Service 🎤 (01:28:10) Studio Overview 🎤 (01:29:39) Studio Compute 🎤 (01:30:48) Studio Data Labeling 🎤 (01:31:45) Data Stores 🎤 (01:32:34) Datasets 🎤 (01:33:44) Experiments 🎤 (01:34:16) Pipelines 🎤 (01:35:23) ML Designer 🎤 (01:36:07) Model Registry 🎤 (01:36:34) Endpoints 🎤 (01:37:50) Notebooks ☁️ AutoML 🎤 (01:38:41) Introduction to AutoML 🎤 (01:41:15) Data Guard Rails 🎤 (01:42:01) Automatic Featurization 🎤 (01:43:53) Model Selection 🎤 (01:44:57) Explanation 🎤 (01:45:51) Primary Metrics 🎤 (01:47:43) Validation Type ☁️ Custom Vision 🎤 (01:48:14) Introduction to Custom Vision 🎤 (01:48:58) Project Types and Domains 🎤 (01:51:54) Custom Vision Features ☁️ Features of generative AI solutions 🎤 (01:54:32) AI vs Generative AI 🎤 (01:57:17) What is a LLM Large Language Model 🎤 (01:58:58) Transformer models 🎤 (02:00:14) Tokenization 🎤 (02:01:26) Embeddings 🎤 (02:02:46) Positional encoding 🎤 (02:04:27) Attention ☁️ Capabilities of Azure OpenAI Service 🎤 (02:08:01) Introduction to Azure OpenAI Service 🎤 (02:10:29) Azure OpenAI Studio 🎤 (02:11:44) Azure OpenAI service pricing 🎤 (02:13:14) What are Copilots 🎤 (02:15:43) Prompt engineering 🎤 (02:18:51) Grounding 🎤 (02:20:36) Copilot demo ☁️ Follow Alongs 🎤 (02:24:04) Setup 🎤 (02:35:02) Computer Vision 🎤 (02:38:44) Custom Vision Classification 🎤 (02:45:22) Custom Vision Object Detection 🎤 (02:51:18) Face Service 🎤 (02:54:30) Form Recognizer 🎤 (02:58:01) OCR 🎤 (03:02:54) Text Analysis 🎤 (03:06:37) QnAMaker 🎤 (03:25:11) LUIS 🎤 (03:30:56) AutoML 🎤 (03:48:13) Designer 🎤 (03:58:31) MNIST 🎤 (04:18:10) Data Labeling 🎤 (04:22:38) Clean up
2024年02月21日
00:00:00 - 04:23:51
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 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 - https://colab.research.google.com/drive/1F_EWVKa8rbMXi3_fG0w7AtcscFq7Hi7B#forceEdit=true&sandboxMode=true 📗 Module 3: Core Learning Algorithms - https://colab.research.google.com/drive/15Cyy2H7nT40sGR7TBN5wBvgTd57mVKay#forceEdit=true&sandboxMode=true 📘 Module 4: Neural Networks with TensorFlow - https://colab.research.google.com/drive/1m2cg3D1x3j5vrFc-Cu0gMvc48gWyCOuG#forceEdit=true&sandboxMode=true 📙 Module 5: Deep Computer Vision - https://colab.research.google.com/drive/1ZZXnCjFEOkp_KdNcNabd14yok0BAIuwS#forceEdit=true&sandboxMode=true 📔 Module 6: Natural Language Processing with RNNs - https://colab.research.google.com/drive/1ysEKrw_LE2jMndo1snrZUh5w87LQsCxk#forceEdit=true&sandboxMode=true 📒 Module 7: Reinforcement Learning - 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
2020年03月04日
00:00:00 - 06:52:08
Machine Learning Full Course - Learn Machine Learning 10 Hours | Machine Learning Tutorial | Edureka

Machine Learning Full Course - Learn Machine Learning 10 Hours | Machine Learning Tutorial | Edureka

🔥 Machine Learning Engineer Masters Program (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/masters-program/machine-learning-engineer-training This Edureka Machine Learning Full Course video will help you understand and learn Machine Learning Algorithms in detail. This Machine Learning Tutorial is ideal for both beginners as well as professionals who want to master Machine Learning Algorithms. Below are the topics covered in this Machine Learning Tutorial for Beginners video: 00:00 Introduction to Machine Learning Full Course 2:47 What is Machine Learning? 4:08 AI vs ML vs Deep Learning 5:43 How does Machine Learning works? 6:18 Types of Machine Learning 6:43 Supervised Learning 8:38 Supervised Learning Examples 11:49 Unsupervised Learning 13:54 Unsupervised Learning Examples 16:09 Reinforcement Learning 18:39 Reinforcement Learning Examples 19:34 AI vs Machine Learning vs Deep Learning 22:09 Examples of AI 23:39 Examples of Machine Learning 25:04 What is Deep Learning? 25:54 Example of Deep Learning 27:29 Machine Learning vs Deep Learning 33:49 Jupyter Notebook Tutorial 34:49 Installation 50:24 Machine Learning Tutorial 51:04 Classification Algorithm 51:39 Anomaly Detection Algorithm 52:14 Clustering Algorithm 53:34 Regression Algorithm 54:14 Demo: Iris Dataset 1:12:11 Stats & Probability for Machine Learning 1:16:16 Categories of Data 1:16:36 Qualitative Data 1:17:51 Quantitative Data 1:20:55 What is Statistics? 1:23:25 Statistics Terminologies 1:24:30 Sampling Techniques 1:27:15 Random Sampling 1:28:05 Systematic Sampling 1:28:35 Stratified Sampling 1:29:35 Types of Statistics 1:32:21 Descriptive Statistics 1:37:36 Measures of Spread 1:44:01 Information Gain & Entropy 1:56:08 Confusion Matrix 2:00:53 Probability 2:03:19 Probability Terminologies 2:04:55 Types of Events 2:05:35 Probability of Distribution 2:10:45 Types of Probability 2:11:10 Marginal Probability 2:11:40 Joint Probability 2:12:35 Conditional Probability 2:13:30 Use-Case 2:17:25 Bayes Theorem 2:23:40 Inferential Statistics 2:24:00 Point Estimation 2:26:50 Interval Estimate 2:30:10 Margin of Error 2:34:20 Hypothesis Testing 2:41:25 Supervised Learning Algorithms 2:42:40 Regression 2:44:05 Linear vs Logistic Regression 2:49:55 Understanding Linear Regression Algorithm 3:11:10 Logistic Regression Curve 3:18:34 Titanic Data Analysis 3:58:39 Decision Tree 3:58:59 what is Classification? 4:01:24 Types of Classification 4:08:35 Decision Tree 4:14:20 Decision Tree Terminologies 4:18:05 Entropy 4:44:05 Credit Risk Detection Use-case 4:51:45 Random Forest 5:00:40 Random Forest Use-Cases 5:04:29 Random Forest Algorithm 5:16:44 KNN Algorithm 5:20:09 KNN Algorithm Working 5:27:24 KNN Demo 5:35:05 Naive Bayes 5:40:55 Naive Bayes Working 5:44:25Industrial Use of Naive Bayes 5:50:25 Types of Naive Bayes 5:51:25 Steps involved in Naive Bayes 5:52:05 PIMA Diabetic Test Use Case 6:04:55 Support Vector Machine 6:10:20 Non-Linear SVM 6:12:05 SVM Use-case 6:13:30 k Means Clustering & Association Rule Mining 6:16:33 Types of Clustering 6:17:34 K-Means Clustering 6:17:59 K-Means Working 6:21:54 Pros & Cons of K-Means Clustering 6:23:44 K-Means Demo 6:28:44 Hierarchical Clustering 6:31:14 Association Rule Mining 6:34:04 Apriori Algorithm 6:39:19 Apriori Algorithm Demo 6:43:29 Reinforcement Learning 6:46:39 Reinforcement Learning: Counter-Strike Example 6:53:59 Markov's Decision Process 6:58:04 Q-Learning 7:02:39 The Bellman Equation 7:12:14 Transitioning to Q-Learning 7:17:29 Implementing Q-Learning 7:23:33 Machine Learning Projects 7:38:53 Who is a ML Engineer? 7:39:28 ML Engineer Job Trends 7:40:43 ML Engineer Salary Trends 7:42:33 ML Engineer Skills 7:44:08 ML Engineer Job Description 7:45:53 ML Engineer Resume 7:54:48 Machine Learning Interview Questions 🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py Edureka Machine Learning Training 🔵 Machine Learning Course using Python: http://bit.ly/38BaJco 🔵 Machine Learning Engineer Masters Program: http://bit.ly/2UYS46r 🔵Python Masters Program: https://bit.ly/3cVibjY 🔵 Python Programming Training: http://bit.ly/38ykZCg 🔵 Data Scientist Masters Program: http://bit.ly/31ZsWOn 🔴 Subscribe to our channel to get latest video updates: https://goo.gl/6ohpTV ⏩ NEW Top 10 Technologies To Learn In 2024 - https://www.youtube.com/watch?v=vaLXPv0ewHU 📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates 📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain 📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka 📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: https://www.instagram.com/edureka_learning/ 📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/ 📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN 📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN 📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/ 📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/ For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: +18338555775 (toll-free). #yt:cc=on #Machine Learning Full Course #machine learning #Machine Learning tutorial #learn machine learning #machine learning for beginners #Machine Learning tutorial for beginners #machine learning for beginner to advance #machine learning course #machine learning complete course #machine learning python #machine learning algorithms #edureka #edureka machine learning #edureka data science #edureka python #what is machine learning #machine learning edureka
2019年09月22日
00:00:00 - 09:38:32
What Is Artificial Intelligence? | Artificial Intelligence (AI) In 10 Minutes | Edureka

What Is Artificial Intelligence? | Artificial Intelligence (AI) In 10 Minutes | Edureka

🔥 Machine Learning Masters Program (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/masters-program/machine-learning-engineer-training This edureka video on Artificial Intelligence will help you understand the concept of AI and how it is used in the real world to solve complex data driven problems. The following topics are covered in this video: 1. What Is Artificial Intelligence? 2. Types Of Artificial Intelligence 3. Applications Of Artificial Intelligence 🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝐲𝐭𝐡𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠𝐬 🔵 Python Programming Certification: http://bit.ly/37rEsnA 🔵 Python Certification Training for Data Science: http://bit.ly/2Gj6fux 🔴. 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 🔵 Data Scientist Masters Program: http://bit.ly/2t1snGM 🔵 Machine Learning Engineer Masters Program: https://bit.ly/3Hi1sXN 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh 🔵 University of Cambridge Online Certifications: https://bit.ly/3RSNTXi 📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 𝟐𝟎𝟐𝟒 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢 ⏩ NEW Top 10 Technologies To Learn In 2024 - https://www.youtube.com/watch?v=vaLXPv0ewHU 📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates 📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain 📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka 📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: https://www.instagram.com/edureka_learning/ 📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/ 📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN 📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN 📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/ 📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/ About the Masters Program Edureka’s Machine Learning Certification Training using Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. This Machine Learning using Python Training exposes you to concepts of Statistics, Time Series and different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. Throughout the Data Science Certification Course, you’ll be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR. ------------------------------------------------------------------------------- Why Go for this Course? Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning --------------------------------------------------------------------------------- Who should go for this course? Edureka’s Python Machine Learning Certification Course is a good fit for the below professionals: Developers aspiring to be a ‘Machine Learning Engineer' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Machine Learning (ML) Techniques Information Architects who want to gain expertise in Predictive Analytics 'Python' professionals who want to design automatic predictive models Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free) #yt:cc=on #artificial intelligence #ai #AI In 10 Minutes #learn artificial intelligence #what is artificial intelligence #types of artificial intelligence #artificial intelligence tutorial #ai tutorial #what is ai #introduction to artificial intelligence #artificial intelligence explained #ai explained #ai applications #introduction to ai #what is ai technology #edureka #ai edureka #deep learning edureka #artificial intelligence for beginners #Artificial intelligence edureka
2019年05月08日
00:00:00 - 00:11:16
NoSQL Database Tutorial – Full Course for Beginners

NoSQL Database Tutorial – Full Course for Beginners

In this NoSQL course, Ania Kubow will be demystifying what NoSQL is, clarify the difference between SQL and NoSQL, as well as going into a deep dive of the 4 main types of NoSQL Databases. With each dive we will be approaching each learning as an ‘explanation’, ‘example’ and ‘exercise’. So the 3 E’s, in order to fully grasp the topic we are discussing. We will finish off with 2 projects for using NoSQL databases as well as guidance on where to go next. 🎉 Thanks to DataStax for providing a grant that made this course possible. 🔗 Register for a free Astra DB account to use for this tutorial: https://astra.datastax.com/register/v11/introtonosql ✏️ Ania Kubow’s channel: https://www.youtube.com/aniakubow 🎥 Intro to NoSQL Course from DataStax: https://www.youtube.com/watch?v=vkSqkLPm5aM ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Introduction ⌨️ (0:01:18) What is NoSQL? ⌨️ (0:04:33) Why use noSQL? ⌨️ (0:06:26) SQL vs NoSQL ⌨️ (0:09:00) Setting Up ⌨️ (0:13:44) Tabular Type ⌨️ (0:37:28) Document Type ⌨️ (0:59:22) Key-value Type ⌨️ (01:11:48) Graph Type ⌨️ (01:22:22) Multi-model Type explained ⌨️ (1:23:52) Project 1 ⌨️ (2:19:36) Project 2 ⌨️ (2:53:51) Where to go next ⭐️ Links ⭐️ 🔗 DataStax Astra DB: https://www.datastax.com/ 🔗 DataStax Enterprise Graph: https://www.datastax.com/products/datastax-graph 🔗 HTTP Status Dogs: https://httpstatusdogs.com/ 🔗 Graph Database Demo: https://github.com/datastaxdevs/workshop-introduction-to-nosql 🔗 Project 1: https://github.com/kubowania/burger-app 🔗 Project 2: https://github.com/kubowania/hotel-app 🔗 Tik Tok Clone using Document SDK: https://www.youtube.com/watch?v=IATOicvih5A 🔗 Netflix Clone with GQL Pagination: https://www.youtube.com/watch?v=g8COh40v2jU 🔗 GraphQL Federation Crypto App: https://www.youtube.com/watch?v=T722_t-HTFw 🎉 Thanks to our Champion and Sponsor supporters: 👾 Raymond Odero 👾 Agustín Kussrow 👾 aldo ferretti 👾 Otis Morgan 👾 DeezMaster -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news
2021年11月29日
00:00:00 - 02:54:53
Stanford CS224N: NLP with Deep Learning | Winter 2021 | Lecture 1 - Intro & Word Vectors

Stanford CS224N: NLP with Deep Learning | Winter 2021 | Lecture 1 - Intro & Word Vectors

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/3w46jar This lecture covers: 1. The course (10min) 2. Human language and word meaning (15 min) 3. Word2vec algorithm introduction (15 min) 4. Word2vec objective function gradients (25 min) 5. Optimization basics (5min) 6. Looking at word vectors (10 min or less) Key learning: The (really surprising!) result that word meaning can be representing rather well by a large vector of real numbers. This course will teach: 1. The foundations of the effective modern methods for deep learning applied to NLP. Basics first, then key methods used in NLP: recurrent networks, attention, transformers, etc. 2. A big picture understanding of human languages and the difficulties in understanding and producing them 3. An understanding of an ability to build systems (in Pytorch) for some of the major problems in NLP. Word meaning, dependency parsing, machine translation, question answering. To learn more about this course visit: https://online.stanford.edu/courses/cs224n-natural-language-processing-deep-learning To follow along with the course schedule and syllabus visit: http://web.stanford.edu/class/cs224n/ Professor Christopher Manning Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science Director, Stanford Artificial Intelligence Laboratory (SAIL) 0:00 Introduction 1:43 Goals 3:10 Human Language 10:07 Google Translate 10:43 GPT 14:13 Meaning 16:19 Wordnet 19:11 Word Relationships 20:27 Distributional Semantics 23:33 Word Embeddings 27:31 Word tovec 37:55 How to minimize loss 39:55 Interactive whiteboard 41:10 Gradient 48:50 Chain Rule #Natural language #Natural Language Processing #Deep Learning #Stanford AI Lectures #Stanford Graduate courses #Computer science #language understanding #Stanford #Stanford Online
2021年10月29日
00:00:00 - 01:24:27