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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
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
Generative AI Full Course - 10 Hours [2024] | Generative AI Course for Beginners | Edureka

Generative AI Full Course - 10 Hours [2024] | Generative AI Course for Beginners | Edureka

🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py 🔥𝐏𝐑𝐎𝐌𝐏𝐓 𝐄𝐍𝐆𝐈𝐍𝐄𝐄𝐑𝐈𝐍𝐆 𝐖𝐈𝐓𝐇 𝐆𝐄𝐍𝐄𝐑𝐀𝐓𝐈𝐕𝐄 𝐀𝐈: https://www.edureka.co/prompt-engineering-generative-ai-course Explore the fundamentals and applications of Generative AI in this comprehensive Generative AI Full course, featuring tutorials on essential concepts such as Text Classification, Autoencoders, GANs, and models like ChatGPT. Delve into the basics of Generative AI, Artificial Intelligence, Machine Learning, and Deep Learning while exploring popular tools like TensorFlow and Keras. Gain insights into various types of AI and learn how to become an AI Engineer. 00:00:00 Introduction 00:00:52 Agenda 00:02:44 What is Generative AI ? 00:04:34 Advantages of Generative AI 00:40:14 The future of generative AI 00:55:41 Autoencoders 01:15:42 What are Generative Models 01:52:44 What is Cha 02:17:11 AI and Culture 02:37:12 What does the Future hold 02:52:01 Simple Relational Knowledge 03:05:21 AI vs ML vd DL 03:19:26 What is AI , types of AI , application of AI 03:57:57 Why Python for AI 04:13:31 Demand of AI 04:23:12 Machine Learning and its details 04:42:20 Machine learning algorithm 04:57:14 Limitation of ML 05:25:23 Natural Language Processing 05:40:12 Deep learning 05:49:50 What is Object Detection 05:58:44 What are tensors 06:17:15 CNN 06:38:44 What are Artificial Nueral Networks 06:53:33 training sa Neural Network 07:06:09 why not feedforward netwrok 07:18:45 Long short term Memory networks 07:34:19 What are Keras? 08:00:15 Cognitive Computing 08:23:13 AI in Different fields 08:35:48 Top 10 Artificial Intelligence Technologies 09:00:14 Role,skills and career in ai 09:09:53 Interview questions 📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 𝟐𝟎𝟐𝟒 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢 ⏩ NEW Top 10 Technologies To Learn In 2024 - https://www.youtube.com/watch?v=vaLXPv0ewHU 🔴 Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 🔵 DevOps Online Training: http://bit.ly/3VkBRUT 🌕 AWS Online Training: http://bit.ly/3ADYwDY 🔵 React Online Training: http://bit.ly/3Vc4yDw 🌕 Tableau Online Training: http://bit.ly/3guTe6J 🔵 Power BI Online Training: http://bit.ly/3VntjMY 🌕 Selenium Online Training: http://bit.ly/3EVDtis 🔵 PMP Online Training: http://bit.ly/3XugO44 🌕 Salesforce Online Training: http://bit.ly/3OsAXDH 🔵 Cybersecurity Online Training: http://bit.ly/3tXgw8t 🌕 Java Online Training: http://bit.ly/3tRxghg 🔵 Big Data Online Training: http://bit.ly/3EvUqP5 🌕 RPA Online Training: http://bit.ly/3GFHKYB 🔵 Python Online Training: http://bit.ly/3Oubt8M 🔵 GCP Online Training: http://bit.ly/3VkCzS3 🌕 Microservices Online Training: http://bit.ly/3gxYqqv 🔵 Data Science Online Training: http://bit.ly/3V3nLrc 🌕 CEHv12 Online Training: http://bit.ly/3Vhq8Hj 🔵 Angular Online Training: http://bit.ly/3EYcCTe 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 🔵 DevOps Engineer Masters Program: http://bit.ly/3Oud9PC 🌕 Cloud Architect Masters Program: http://bit.ly/3OvueZy 🔵 Data Scientist Masters Program: http://bit.ly/3tUAOiT 🌕 Big Data Architect Masters Program: http://bit.ly/3tTWT0V 🔵 Machine Learning Engineer Masters Program: http://bit.ly/3AEq4c4 🌕 Business Intelligence Masters Program: http://bit.ly/3UZPqJz 🔵 Python Developer Masters Program: http://bit.ly/3EV6kDv 🌕 RPA Developer Masters Program: http://bit.ly/3OteYfP 🔵 Web Development Masters Program: http://bit.ly/3U9R5va 🌕 Computer Science Bootcamp Program: http://bit.ly/3UZxPBy 🔵 Cyber Security Masters Program: http://bit.ly/3U25rNR 🌕 Full Stack Developer Masters Program: http://bit.ly/3tWCE2S 🔵 Automation Testing Engineer Masters Program: http://bit.ly/3AGXg2J 🌕 Python Developer Masters Program: https://bit.ly/3EV6kDv 🔵 Azure Cloud Engineer Masters Program: http://bit.ly/3AEBHzH 🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬 🔵 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT 🌕 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh 🔵 Advanced Certificate Program in Cloud Computing with E&ICT Academy, IIT Guwahati: https://bit.ly/43vmME8 🌕Advanced Certificate Program in Cybersecurity with E&ICT Academy, IIT Guwahati: https://bit.ly/3Pd2utG 📌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/ Please write back to us at [email protected] or call us at IND: 9606058406 / US: +18338555775 (toll-free) for more information. #yt:cc=on #generative ai #generative ai full course #generative ai course #generativeAI #generative AI full course #generative ai tutorial #generative ai explained #generative AI for beginners #genai #chatgpt #googlebard #OpenAI #AI #artificial intelligence #Convolutional Neural Network #Cognitive AI #AI Tool #generative ai tools #text classification #Ai tutorial for beginners #artificial intelligence training #edureka #generative ai video #genai course #gen ai tutorial
2024年03月08日
00:00:00 - 10:14:14
Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial

Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial

This course will teach you how to use Keras, a neural network API written in Python and integrated with TensorFlow. We will learn how to prepare and process data for artificial neural networks, build and train artificial neural networks from scratch, build and train convolutional neural networks (CNNs), implement fine-tuning and transfer learning, and more! ⭐️🦎 COURSE CONTENTS 🦎⭐️ ⌨️ (00:00:00) Welcome to this course ⌨️ (00:00:16) Keras Course Introduction ⌨️ (00:00:50) Course Prerequisites ⌨️ (00:01:33) DEEPLIZARD Deep Learning Path ⌨️ (00:01:45) Course Resources ⌨️ (00:02:30) About Keras ⌨️ (00:06:41) Keras with TensorFlow - Data Processing for Neural Network Training ⌨️ (00:18:39) Create an Artificial Neural Network with TensorFlow's Keras API ⌨️ (00:24:36) Train an Artificial Neural Network with TensorFlow's Keras API ⌨️ (00:30:07) Build a Validation Set With TensorFlow's Keras API ⌨️ (00:39:28) Neural Network Predictions with TensorFlow's Keras API ⌨️ (00:47:48) Create a Confusion Matrix for Neural Network Predictions ⌨️ (00:52:29) Save and Load a Model with TensorFlow's Keras API ⌨️ (01:01:25) Image Preparation for CNNs with TensorFlow's Keras API ⌨️ (01:19:22) Build and Train a CNN with TensorFlow's Keras API ⌨️ (01:28:42) CNN Predictions with TensorFlow's Keras API ⌨️ (01:37:05) Build a Fine-Tuned Neural Network with TensorFlow's Keras API ⌨️ (01:48:19) Train a Fine-Tuned Neural Network with TensorFlow's Keras API ⌨️ (01:52:39) Predict with a Fine-Tuned Neural Network with TensorFlow's Keras API ⌨️ (01:57:50) MobileNet Image Classification with TensorFlow's Keras API ⌨️ (02:11:18) Process Images for Fine-Tuned MobileNet with TensorFlow's Keras API ⌨️ (02:24:24) Fine-Tuning MobileNet on Custom Data Set with TensorFlow's Keras API ⌨️ (02:38:59) Data Augmentation with TensorFlow' Keras API ⌨️ (02:47:24) Collective Intelligence and the DEEPLIZARD HIVEMIND ⭐️🦎 DEEPLIZARD COMMUNITY RESOURCES 🦎⭐️ 👉 Check out the blog post and other resources for this course: 🔗 https://deeplizard.com/learn/video/RznKVRTFkBY 💻 DOWNLOAD ACCESS TO CODE FILES 🤖 Available for members of the deeplizard hivemind: 🔗 https://deeplizard.com/resources 🧠 Support collective intelligence, join the deeplizard hivemind: 🔗 https://deeplizard.com/hivemind 👋 Hey, we're Chris and Mandy, the creators of deeplizard! 👀 CHECK OUT OUR VLOG: 🔗 https://youtube.com/deeplizardvlog 👀 Follow deeplizard: YouTube: https://youtube.com/deeplizard Our vlog: https://youtube.com/deeplizardvlog Facebook: https://facebook.com/deeplizard Instagram: https://instagram.com/deeplizard Twitter: https://twitter.com/deeplizard Patreon: https://patreon.com/deeplizard 🎵 deeplizard uses music by Kevin MacLeod 🔗 https://youtube.com/channel/UCSZXFhRIx6b0dFX3xS8L1yQ 🔗 http://incompetech.com/ ❤️ Please use the knowledge gained from deeplizard content for good, not evil. -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news #karas #tensorflow #deep learning #machine learning
2020年06月18日
00:00:00 - 02:47:55
Cancer Detection Using Deep Learning | Deep Learning Projects | Deep Learning Training | Edureka

Cancer Detection Using Deep Learning | Deep Learning Projects | Deep Learning Training | Edureka

🔥Edureka Deep Learning With TensorFlow (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎): https://www.edureka.co/ai-deep-learning-with-tensorflow This Edureka video on 𝐂𝐚𝐧𝐜𝐞𝐫 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐔𝐬𝐢𝐧𝐠 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠, will help you understand how to develop models using Convolution Neural Networks. We will also have a discussion on improving model accuracy using pretrained models. Below are the topics covered in Cancer Detection Using Deep Learning video : 00:00:00 Introduction 00:00:52 Introduction to Deep Learning 00:02:57 Deep Learning General Intuition 00:05:43 Image Processing Using DL 00:16:54 Brain Tumor Detection Using Custom Model 01:07:43 Transfer Learning 01:11:21 CNN Architectures 🔹Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE 🔹Check our complete Deep Learning With TensorFlow Blog Series: http://bit.ly/2sqmP4s 🔴Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV 📌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/ #edureka #edurekadeeplearning #deeplearningwithtensorflow #cancerdetectionusingdeeplearning #convolutionneuralnetworks #deeplearningpretrainedmodels #deepearningtutorial #edurekatraining ---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧--------- 🔵 Data Science Online Training: https://bit.ly/2NCT239 🟣 Python Online Training: https://bit.ly/2CQYGN7 🔵 AWS Online Training: https://bit.ly/2ZnbW3s 🟣 RPA Online Training: https://bit.ly/2Zd0ac0 🔵 DevOps Online Training: https://bit.ly/2BPwXf0 🟣 Big Data Online Training: https://bit.ly/3g8zksu 🔵 Java Online Training: https://bit.ly/31rxJcY ---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬--------- 🟣Machine Learning Engineer Masters Program: https://bit.ly/388NXJi 🔵DevOps Engineer Masters Program: https://bit.ly/2B9tZCp 🟣Cloud Architect Masters Program: https://bit.ly/3i9z0eJ 🔵Data Scientist Masters Program: https://bit.ly/2YHaolS 🟣Big Data Architect Masters Program: https://bit.ly/31qrOVv 🔵Business Intelligence Masters Program: https://bit.ly/2BPLtn2 -----------------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝐆𝐏 𝐂𝐨𝐮𝐫𝐬𝐞𝐬--------------- 🔵Artificial and Machine Learning PGP: https://bit.ly/2Ziy7b1 🟣CyberSecurity PGP: https://bit.ly/3eHvI0h 🔵Digital Marketing PGP: https://bit.ly/38cqdnz 🟣Big Data Engineering PGP: https://bit.ly/3eTSyBC 🔵Data Science PGP: https://bit.ly/3dIeYV9 🟣Cloud Computing PGP: https://bit.ly/2B9tHLP ---------------------------------- 🔅🔅How it Works? 1. This is a 5 Week Instructor led Online Course. 2. Course consists of 30 hours of online classes, 20 hours of assignment, 20 hours of project 3. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 4. You will get Lifetime Access to the recordings in the LMS. 5. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - 🔅🔅About the Course Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. - - - - - - - - - - - - - - For Online Training and Certification, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information #yt:cc=on #cancer detection using deep learning #cancer detection using machine learning #cancer detection using ct scan #cancer detection using cnn #cnn #covid detection using convolution neural networks #brain tumor detection using deep learning #cancer and machine learning #cancer detection #deep learning tumor prediction #deep learning in health care #deep learning projects #deep learning training #edureka deep learning #edureka machine learning #edureka
2021年06月27日
00:00:00 - 01:30:33
Scikit-Learn Course - Machine Learning in Python Tutorial

Scikit-Learn Course - Machine Learning in Python Tutorial

Scikit-learn is a free software machine learning library for the Python programming language. Learn about machine learning using scikit-learn in this full course. 💻 Code: https://github.com/DL-Academy/MachineLearningSKLearn 🔗 Scikit-learn website: https://scikit-learn.org ✏️ Course from DL Academy. Check out their YouTube channel: https://www.youtube.com/channel/UCTgBlZ1fmNa87NUY1xvoxpg 🔗 View more courses here: https://thedlacademy.com/ ⭐️ Course Contents ⭐️ Chapter 1 - Getting Started with Machine Learning ⌨️ (0:00) Introduction ⌨️ (0:22) Installing SKlearn ⌨️ (3:37) Plot a Graph ⌨️ (7:33) Features and Labels_1 ⌨️ (11:45) Save and Open a Model Chapter 2 - Taking a look at some machine learning algorithms ⌨️ (13:47) Classification ⌨️ (17:28) Train Test Split ⌨️ (25:31) What is KNN ⌨️ (33:48) KNN Example ⌨️ (43:54) SVM Explained ⌨️ (51:11) SVM Example ⌨️ (57:46) Linear regression ⌨️ (1:07:49) Logistic vs linear regression ⌨️ (1:23:12) Kmeans and the math beind it ⌨️ (1:31:08) KMeans Example Chapter 3 - Artificial Intelligence and the science behind It ⌨️ (1:42:02) Neural Network ⌨️ (1:56:03) Overfitting and Underfitting ⌨️ (2:03:05) Backpropagation ⌨️ (2:18:16) Cost Function and Gradient Descent ⌨️ (2:26:24) CNN ⌨️ (2:31:46) Handwritten Digits Recognizer -- 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06月24日
00:00:00 - 02:54:25
Emotion Detection using OpenCV & Python | Real time Emotion Detection | Deep Learning | Edureka

Emotion Detection using OpenCV & Python | Real time Emotion Detection | Deep Learning | Edureka

🔥Edureka PG Diploma in Artificial Intelligence & ML from E & ICT Academy NIT Warangal(Use Code: YOUTUBE20): https://www.edureka.co/executive-programs/machine-learning-and-ai This Edureka video on 'Emotion Detection using OpenCV & Python' will give you an overview of Emotion Detection using OpenCV & Python and will help you understand various important concepts that concern Emotion Detection using OpenCV & Python Following pointers are covered in this Emotion Detection using OpenCV & Python: 00:00:00 Agenda 00:01:54 Introduction to Deep Learning 00:04:14 What is Image Processing? 00:04:58 Libraries used in Project 00:07:30 Steps to execute the Project 00:08:47 Implementation ------------------------------------ Github link for codes: https://github.com/dhruvpandey662/Emotion-detection dataset link: https://www.dropbox.com/s/w3zlhing4dkgeyb/train.zip?dl=0 ------------------------------------ 🔹Check Edureka's Deep Learning & TensorFlow Tutorial playlist here: https://goo.gl/cck4hE 🔹Check Edureka's Deep Learning & TensorFlow Tutorial Blog Series: http://bit.ly/2sqmP4s 🔴Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV 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/ ---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧--------- 🔵 Data Science Online Training: https://bit.ly/2NCT239 🟣 Python Online Training: https://bit.ly/2CQYGN7 🔵 AWS Online Training: https://bit.ly/2ZnbW3s 🟣 RPA Online Training: https://bit.ly/2Zd0ac0 🔵 DevOps Online Training: https://bit.ly/2BPwXf0 🟣 Big Data Online Training: https://bit.ly/3g8zksu 🔵 Java Online Training: https://bit.ly/31rxJcY ---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬--------- 🟣Machine Learning Engineer Masters Program: https://bit.ly/388NXJi 🔵DevOps Engineer Masters Program: https://bit.ly/2B9tZCp 🟣Cloud Architect Masters Program: https://bit.ly/3i9z0eJ 🔵Data Scientist Masters Program: https://bit.ly/2YHaolS 🟣Big Data Architect Masters Program: https://bit.ly/31qrOVv 🔵Business Intelligence Masters Program: https://bit.ly/2BPLtn2 -----------------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏GD 𝐂𝐨𝐮𝐫𝐬𝐞𝐬--------------- 🔵Artificial and Machine Learning PGD: https://bit.ly/2Ziy7b1 #edureka #edurekadeeplearning #deeplearning #EmotionDetectionusingOpenCV&Python #RealTimeEmotionDetection #machinelearningpretrainedmodels #deeplearningtutorial #edurekatraining -------------------------------------------------------------------- Why Machine Learning & AI? Because of the increasing need for intelligent and accurate decision making, there is an exponential growth in the adoption of AI and ML technologies. Hence these are poised to remain the most important technologies in the years to come. ----------------------------------------------- PG Program in Machine Learning & AI 1. Ranked 4th among NITs by NIRF 2. Ranked among Top 50 Institutes in India 3. Designated as Institute of National Importance ----------------------------------------------- Program Features 1. Mentorship from NITW faculty 2. Placement Assistance 3. Alumni Status 4. Industry Networking ----------------------------------------------- Industry Projects 1. Building a Conversational ChatBot 2. Predictive Model for Auto Insurance 3. E-commerce Website - Sales Prediction ----------------------------------------------- Mentors & Instructors Dr. RBV Subramaanyam Professor NITW Dr. DVLN Somayajulu Professor NITW Dr. P. Radha Krishna Professor NITW Dr. V. Ravindranath Professor JNTU Kakinada -------------------------------------------- Is this program for me? If you’re passionate about AI & ML and want to pursue a career in this field, this program is for you. Whether you’re a fresher or a professional, this program is designed to equip you with the skills you need to rise to the top in a career in AI & ML. Is there any eligibility criteria for this program? A potential candidate must have one of the following prerequisites: Degrees like BCA, MCA, and B.Tech or Programming experience Should have studied PCM in 10+2 Will I get any certificate at the end of the course? Yes, you will receive a Post-Graduate industry-recognized certificate from E & ICT Academy, NIT Warangal upon successful completion of the course. For more information, please write back to us at [email protected] or call us at IND: +91-9606058418 / US: 18338555775 #yt:cc=on #emotion detection using opencv & python #emotion detection #how to detect emotions #facial emotion recognition #emotion recognition technology #emotion detection using opencv python #human emotion detection from image #image processing #emotion detection project #python emotion detection #emotion detection using ai #emotion detection using deep learning #emotion detection python #emotion detection using cnn #deep learning #machine learning #edureka
2021年09月17日
00:00:00 - 00:20:02
Convolutional Neural Networks - Deep Learning basics with Python, TensorFlow and Keras p.3

Convolutional Neural Networks - Deep Learning basics with Python, TensorFlow and Keras p.3

Welcome to a tutorial where we'll be discussing Convolutional Neural Networks (Convnets and CNNs), using one to classify dogs and cats with the dataset we built in the previous tutorial. Text tutorials and sample code: https://pythonprogramming.net/convolutional-neural-network-deep-learning-python-tensorflow-keras/ Discord: https://discord.gg/sentdex Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex G+: https://plus.google.com/+sentdex #TensorFlow #Keras #Deep Learning #tutorial #neural network #machine learning
2018年08月19日
00:00:00 - 00:18:39
Deep Learning Full Course - Learn Deep Learning in 6 Hours | Deep Learning Tutorial | Edureka

Deep Learning Full Course - Learn Deep Learning in 6 Hours | Deep Learning Tutorial | Edureka

🔥 AI & Deep Learning with TensorFlow (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/ai-deep-learning-with-tensorflow This Edureka Deep Learning Full Course video will help you understand and learn Deep Learning & Tensorflow in detail. This Deep Learning Tutorial is ideal for both beginners as well as professionals who want to master Deep Learning Algorithms. Below are the topics covered in this Deep Learning tutorial video: 00:00 Introduction 3:11 What is Deep Learning 3:55 Why Artificial Intelligence? 5:48 What is AI? 6:53 Applications of AI 8:43 Machine Learning 10:28 Types of Machine Learning 10:33 Supervised Learning 11:43 Unsupervised Learning 13:08 Reinforcement Learning 14:38 Limitations of Machine Learning 16:08 Deep Learning to the Rescue 19:28 What is Deep Learning? 22:58 Deep Learning Example 24:28 Deep Learning Applications 25:48 Deep Learning Tutorial 27:08 Understanding Deep Learning With an Analogy 29:58 How Deep Learning works? 31:12 Why We need Artificial Neuron? 32:58 Perceptron Learning Algorithm 36:13 Types of Activation Functions 41:33 Single Layer Perceptron Use-case 42:33 What is TensorFlow? 44:18 Tensorflow Code Basics 49:08 TensorFlow Example 59:13 What is a Computational Graph? 1:27:08 Limitations of Single Layer Perceptron 1:28:08 Multilayer Perceptron 1:29:18 How it works? 1:29:23 What is Backpropagation? 1:30:23 Backpropagation Learning Algorithm 1:34:43 Multilayer Perceptron Use-case 1:37:48 Top 8 Deep Learning Frameworks 1:38:18 Chainer 1:39:18 CNTK 1:40:48 Caffe 1:42:28 MXNet 1:43:33 Deeplearning4j 1:45:23 Keras 1:46:58 PyTorch 1:48:23 TensorFlow 1:50:23 TensorFlow Tutorial 1:50:43 Rock or Mine Prediction Use-case 1:52:53 How to Create This Model? 1:54:13 What are Tensors? 1:54:38 Tensor Rank 1:55:58 What is TensorFlow? 2:02:28 Graph Visualization 2:05:10 Constant, Placeholder & Variables 2:08:55 Creating A Model 2:17:06 Reducing The Loss 2:18:31 Batch Gradient Descent 2:22:01 Implementing Rock or Mine Prediction Use-case 2:36:24 Artificial Neural Network Tutorial 2:39:29 Why Neural Network? 2:40:29 Problems Before Neural Network 2:42:09 What is Artificial Neural Network? 2:44:04 How It Works? 2:46:24 Perceptron Learning Algorithm - Beer Analogy 2:52:24 Multilayer Perceptron 2:53:34 Artificial Neutral Network 2:54:24 Training A Neural Network 3:05:54 Applications of Network Networks 3:09:04 Backpropagation & Gradient Descent Tutorial 3:09:49 Perceptron 3:10:44 How does the Network Learn? 3:11:09 MNIST Dataset 3:11:59 Cost Function 3:13:54 Finding Local Minima 3:16:09 Gradient Descent Learning 3:17:19 Back Propagation 3:21:29 Recurrent Neural Networks 3:22:04 Why not Feedforward Network? 3:24:29 What is Recurrent Neural Networks? 3:29:24 Training A Recurrent Neural Network 3:29:49 Vanishing & Exploding Gradient Problem 3:34:09 Long Short Term Memory Networks 3:51:04 Convolutional Neural Network 3:51:29 How A Computer Reads An Image? 3:52:14 Why Not Fully Connected Network? 3:53:29 What Convolutional Neural Network? 3:54:04 How CNN Works? 3:54:39 Convolution Layer 3:59:04 ReLU Layer 4:03:49 Fully Connected Layer 4:11:59 Autoencoders Tutorial 4:13:49 PCA vs Autoencoders 4:15:14 Introduction to Autoencoders 4:17:09 Properties of Autoencoders 4:18:09 Training Autoencoders 4:19:14 Architecture of Autoencoders 4:23:49 Types of Autoencoders 4:25:49 Convolutional Autoencoders 4:26:44 Sparse Autoencoders 4:28:29 Deep Autoencoders 4:30:29 Contractive Autoencoders 4:31:54 Demo 4:35:09 Restricted Boltzmann Machine 4:38:54 Working of RBMs 4:40:29 RBM: Energy-Based Model 4:42:34 RBM: Probabilistic Model 4:42:54 RBM Training 4:44:09 RBM: Training to Prediction 4:44:39 RBM: Example 4:46:29 TensorFlow Object Detection 4:47:34 What is Object Detection? 4:48:24 Object Detection Applications 4:51:04 Workflow of Object Detection 4:52:49 Object Detection in TensorFlow 4:53:59 Object Detection Demo 5:10:44 Creating Chatbots Using Tensorflow 5:12:14 What is Chatbots? 5:12:19 How Does ChatBot Works? 5:14:44 Applications of Chatbot 5:15:54 Layers of Chatbot 5:16:14 Natural Language Processing 5:19:59 Demo 5:21:44 Layers of Chatbot 5:21:59 Deep Learning Interview Questions -------------------------------------------------------------------------------------------------------- PG in Artificial Intelligence and Machine Learning with NIT Warangal : https://www.edureka.co/post-graduate/machine-learning-and-ai Post Graduate Certification in Data Science with IIT Guwahati - https://www.edureka.co/post-graduate/data-science-program (450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies) Instagram: https://www.instagram.com/edureka_learning Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). #yt:cc=on #Deep Learning Full Course #Deep Learning Tutorial #deep learning tutorial for beginners #deep learning for beginners #tensorflow tutorial #tensorflow tutorial for beginners #tensorflow deep learning tutorial #deep learning complete tutorial #deep learning algorithms #deep learning applications #deep learning frameworks #deep learning training #edureka #deep learning #deep learning edureka #what is deep learning #deep learning with tensorflow #tensorflow edureka
2019年09月08日
00:00:00 - 06:02:26
PyTorch for Deep Learning - Full Course / Tutorial

PyTorch for Deep Learning - Full Course / Tutorial

In this course, you will learn how to build deep learning models with PyTorch and Python. The course makes PyTorch a bit more approachable for people starting out with deep learning and neural networks. 💻 Code: https://jovian.ml/aakashns/01-pytorch-basics https://jovian.ml/aakashns/02-linear-regression https://jovian.ml/aakashns/03-logistic-regression https://jovian.ml/aakashns/04-feedforward-nn https://jovian.ml/aakashns/05-cifar10-cnn https://jovian.ml/aakashns/05b-cifar10-resnet https://jovian.ml/aakashns/06-mnist-gan ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Introduction ⌨️ (0:03:25) PyTorch Basics & Linear Regression ⌨️ (1:32:15) Image Classification with Logistic Regression ⌨️ (3:06:59) Training Deep Neural Networks on a GPU with PyTorch ⌨️ (4:44:51) Image Classification using Convolutional Neural Networks ⌨️ (6:35:11) Residual Networks, Data Augmentation and Regularization ⌨️ (8:12:08) Training Generative Adverserial Networks (GANs) #deep learning #pytorch #neural network #python
2020年04月30日
00:00:00 - 09:41:40
The Foundations of Entrepreneurship - Full Course

The Foundations of Entrepreneurship - Full Course

This entrepreneurship course will teach you the important lessons that they don't teach you in business school. You will learn about topics such as how to network, how to find customers, and how to get a job. 📒 Get the class workbooks here: https://harouneducation.com/entrepreneurship Chris Haroun teaches this course. Chris has sold more than 1,000,000 of his online business & self improvement courses in 12 languages in 196 countries and his courses have been profiled in Business Insider, NBC, Inc, Forbes, CNN, Entrepreneur & on other business news websites. This course is an amalgamation of business advice that Chris has compiled from his many meetings with successful business people over the past two decades as well as observations of why brilliant entrepreneurs like Steve Jobs or Mark Zuckerberg have become incredibly successful. ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Section 1: Relationships Are More Important Than Product Knowledge ⌨️ (0:34:49) Section 2: Be Long Term Greedy ⌨️ (1:02:26) Section 3: Avoid Burnout ⌨️ (1:19:59) Section 4: Create Off The Charts Confidence; Wear That Super Superman Cape! ⌨️ (1:38:29) Section 5: Ethics. Use It or Lose It ⌨️ (1:55:03) Section 6: Every Battle is Won Before It Has Been Fought ⌨️ (2:05:23) Section 7: Goal Setting ⌨️ (2:17:44) Section 8: Happiness is... ⌨️ (2:42:11) How to Complete Homework 1 ⌨️ (2:47:35) Section 9: Legal Stuff is Important ⌨️ (3:08:38) Section 10: Management Best Practices ⌨️ (3:32:33) Section 11: Navigating Corporate Politics; Swimming with Sharks ⌨️ (4:01:38) Section 12: Only Take Advice from Successful People ⌨️ (4:09:24) Section 13: Only the Paranoid Survive ⌨️ (4:27:07) Section 14: Risk Taking ⌨️ (4:36:55) Section 15: Sales Best Practices ⌨️ (5:03:12) Section 16: Think Different ⌨️ (5:24:07) Section 17: You Be You ⌨️ (5:45:01) How to Complete Homework 2 🎉 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
2022年02月14日
00:00:00 - 05:46:30
YOLO Object Detection Using OpenCV And Python | Python Projects |  Python Training | Edureka

YOLO Object Detection Using OpenCV And Python | Python Projects | Python Training | Edureka

🔥Edureka Python Training: https://www.edureka.co/python-programming-certification-training/ This Edureka video on " YOLO Object Detection Using OpenCV and Python", will walk you through the basics of OpenCV and the implementation of the YOLO algorithm for object detection. The following are covered in this Python & Open CV Tutorial video: 00:00:00 Introduction 00:55:00 Introduction to Computer Vision 00:02:43 A different approach for Object Detection 00:04:27 What is Open-CV? 00:16:25 CNN for Image Processing 00:22:27 YOLO Algorithm 00:24:54 Real-Time object detection using YOLO and Open-CV 🔹Checkout Edureka's Python Tutorial Playlist: https://goo.gl/WsBpKe 🔹Checkout Edureka's Python Tutorial Blog Series: http://bit.ly/2sqmP4s 🔴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 Community: https://bit.ly/EdurekaCommunity Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Telegram: https://t.me/edurekaupdates SlideShare: https://www.slideshare.net/EdurekaIN Meetup: https://www.meetup.com/edureka/ #Edureka #EdurekaPython #YOLOObjectDetection #PythonProjects #PythonProgramming #OpenCVProjects #PythonTutorial #PythonTraining --------------------------- How it Works? 1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive, and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Master the Basic and Advanced Concepts of Python 2. Understand Python Scripts on UNIX/Windows, Python Editors, and IDEs 3. Master the Concepts of Sequences and File operations 4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques, and Regular expressions and using modules in Python 5. Gain expertise in machine learning using Python and build a Real-Life Machine Learning application 6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 7. Master the concepts of MapReduce in Hadoop 8. Learn to write Complex MapReduce programs 9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python 10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop, and/Or MRjob Basics 11. Master the concepts of Web scraping in Python 12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands-on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built-in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicate that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. Who should go for python? The Python Programming Certification Course is a good fit for the below professionals: Programmers, Developers, Technical Leads, Architects, Freshers Data Scientists, Data Analysts Statisticians and Analysts Business Analysts Project Managers Business Intelligence Managers For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 #yt:cc=on #yolo object detection tutorial #yolo object detection python #yolo object detection demo #object detection using yolo #yolo object detection from scratch #yolo python #yolo detection model #yolo object detection #object detection yolo #object detection using python #yolo object detection opencv python #object detection python #object detection using opencv python #opencv python projects #python projects #yolo python opencv #edureka python #Edureka
2021年03月08日
00:00:00 - 00:47:21
Image Classification using CNN | Deep Learning Tutorial | Machine Learning Project 9 | Edureka

Image Classification using CNN | Deep Learning Tutorial | Machine Learning Project 9 | Edureka

🔥 Edureka Machine Learning Certification training (Use Code: YOUTUBE20) : https://www.edureka.co/masters-program/machine-learning-engineer-training This Edureka video on 'Image Classification using CNN' will give you an overview of Image Classification using Machine Learning and will help you understand various important concepts that concern Image Classification with ML. Following pointers are covered in this Image Classification using CNN: 1) Introduction 2) Tools and Frameworks 3) Project ------------------------------------ 🔹Checkout Edureka's Machine Learning Project playlist: https://bit.ly/3ij9Uw7 🔹Checkout Edureka's Machine Learning Python Tutorial playlist: https://bit.ly/3szLTCO 🔹Checkout Edureka's Machine Learning R Tutorial Playlist: https://bit.ly/3duYGlF 🔹Checkout Edureka's Machine Learning Tutorial Blog Series: https://bit.ly/2PX5lIp 🔴Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV 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/ ---------Edureka Machine Learning Projects--------- 🔵 Plant Leaf Disease Detection with GUI: https://bit.ly/36Y6l8g 🔵 House Price Prediction using ML: https://bit.ly/3i0VKzJ 🔵 Emoji Prediction using LSTM: https://bit.ly/2TDuPjR 🔵 Color old photographs using Autoencoders: https://bit.ly/3BQg7r9 🔵 Handwritten Digit Recognition on MNIST dataset: https://bit.ly/3zTCxGf 🔵 Generate Images Using DC-Gan's: https://bit.ly/2TPtYwC 🔵 Building Document Scanner Using OpenCV: https://bit.ly/3lqequN 🔵 Cartoon Effect on Image using OpenCV: https://bit.ly/3rV9rTR ---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧--------- 🔵 Data Science Online Training: https://bit.ly/2NCT239 🟣 Python Online Training: https://bit.ly/2CQYGN7 🔵 AWS Online Training: https://bit.ly/2ZnbW3s 🟣 RPA Online Training: https://bit.ly/2Zd0ac0 ---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬--------- 🟣Machine Learning Engineer Masters Program: https://bit.ly/388NXJi 🟣Cloud Architect Masters Program: https://bit.ly/3i9z0eJ 🔵Data Scientist Masters Program: https://bit.ly/2YHaolS 🟣Big Data Architect Masters Program: https://bit.ly/31qrOVv 🔵Business Intelligence Masters Program: https://bit.ly/2BPLtn2 -----------------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏GD 𝐂𝐨𝐮𝐫𝐬𝐞--------------- 🔵Artificial and Machine Learning PGD: https://bit.ly/2Ziy7b1 #edureka #edurekamachinelearning #machinelearning #ImageClassificationusingCNN #machinelearningproject #machinelearningtutorial #edurekatraining -------------------------------------------------------------------- About the Course : Edureka’s Machine Learning Course using Python is designed to make you grasp the concepts of Machine Learning. The Machine Learning training will provide a deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in the python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience. -------------------------------------- Why Learn Machine Learning with Python? 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. -------------------------------------------------------------------- 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 #Image Classification using CNN #Image Classification using deep learning #cnn image classification #deep learning image classification #convolutional neural networks #Gradient Visualization #Gradient Visualization using Machine Learning #Visualizing Gradient Descent #gradient descent visualization #convolutional neural network for image classification #deep learning #CNN #Neural Network #machine learning #machine learning algorithms #MachineLearningprojects #edureka
2021年08月07日
00:00:00 - 00:44:02
React JS Course for Beginners - 2021 Tutorial

React JS Course for Beginners - 2021 Tutorial

This is a full premium course. Learn React.js from the ground up with fundamentals to more intermediate and advanced topics. You will learn by building a real app! 💻 Starter files: https://github.com/weibenfalk/react-rmdb-v3-starter-files Course from Thomas Weibenfalk. Check out his channel: https://www.youtube.com/channel/UCnnnWy4UTYN258FfVGeXBbg Learn: - React - JSX - Styled Components - React Router - State and Props - Context - CSS - API handling - Hooks - Typescript - Persist state in SessionStorage - Deploy to Netlify - And MUCH more ... ⭐️ Course Contents ⭐️ ⌨️ (0:00:10) Introduction ⌨️ (0:00:57) The App ⌨️ (0:03:27) The Movie DB - API Key ⌨️ (0:05:09) What is React? ⌨️ (0:10:49) Starter Files ⌨️ (0:14:16) Quick about tooling ⌨️ (0:15:50) Bootstrap with CRA ⌨️ (0:19:11) Install dependencies ⌨️ (0:24:17) Copy fils from starter files ⌨️ (0:28:34) Setup API Key and walkthrough of API files ⌨️ (0:33:24) React without JSX ⌨️ (0:40:10) Short about JSX ⌨️ (0:42:52) Crash course in Props and State ⌨️ (0:55:12) Short about Styled Components ⌨️ (0:58:23) Global Styles ⌨️ (1:08:01) Header Component ⌨️ (1:21:09) Header Component - Styles ⌨️ (1:25:40) Home Component - Scaffold ⌨️ (1:33:45) Short about built-in hooks in React ⌨️ (1:38:55) Fetch data from the API for Home Page ⌨️ (1:52:44) Custom hook for Home Page ⌨️ (1:59:49) HeroImage Component ⌨️ (2:11:22) HeroImage Component - Styles ⌨️ (2:20:23) Grid Component ⌨️ (2:26:06) Grid Component - Styles ⌨️ (2:29:44) Thumb Component ⌨️ (2:34:59) Thumb Component - Styles ⌨️ (2:37:28) Spinner Component ⌨️ (2:42:03) SearchBar Component ⌨️ (2:57:54) SearchBar Component - Styles ⌨️ (3:02:16) SearchBar Component - Logic ⌨️ (3:06:51) Button Component ⌨️ (3:10:42) Button Component - Styles ⌨️ (3:13:06) Button Component - Logic ⌨️ (3:19:50) Short about React Router ⌨️ (3:21:59) Routing with React Router ⌨️ (3:34:15) Movie Component - Scaffold ⌨️ (3:36:26) Fetch movie data from the API ⌨️ (3:49:38) BreadCrumb Component ⌨️ (3:54:49) BreadCrumb Component - Styles ⌨️ (3:57:46) MovieInfo Component ⌨️ (4:08:41) MovieInfo Component - Styles ⌨️ (4:17:35) MovieInfoBar Component ⌨️ (4:23:26) MovieInfoBar Component - Styles ⌨️ (4:26:38) Actor Component ⌨️ (4:32:09) Actor Component - Styles ⌨️ (4:34:16) Short about PropTypes ⌨️ (4:37:38) Validate Props with PropTypes ⌨️ (4:48:17) Short about SessionStorage ⌨️ (4:50:32) SessionStorage - Home ⌨️ (4:59:34) SessionStorage - Movie ⌨️ (5:03:17) Build and prepare for Netlify ⌨️ (5:05:58) Netlify drag and drop and Netlify CLI ⌨️ (5:10:29) Netlify Continous Deployment ⌨️ (5:13:48) Bonus - Classes - SearchBar Component ⌨️ (5:22:04) Bonus - Classes - Home Component ⌨️ (5:33:14) Bonus - Classes - Movie Component ⌨️ (5:40:39) Bonus - Typescript - Introduction ⌨️ (5:42:20) Bonus - Typescript - Bootstrap project and copy files ⌨️ (5:47:19) Bonus - Typescript - Refactor base files ⌨️ (6:04:16) Bonus - Typescript - Refactor Home and Components ⌨️ (6:14:55) Bonus - Typescript - Refactor Movie and Components ⌨️ (6:25:52) Bonus - Login - Short about TMDB login and rating system ⌨️ (6:29:25) Bonus - Login - Global Context ⌨️ (6:34:34) Bonus - Login - Login Component ⌨️ (6:50:49) Bonus - Login - Login Component - Styles ⌨️ (6:53:47) Bonus - Login - Login from Header ⌨️ (7:00:27) Bonus - Login - Rate Component ⌨️ (7:04:35) Bonus - Login - Rating from MovieInfo 🎉 Thanks to our Champion and Sponsor supporters: 👾 Wong Voon jinq 👾 hexploitation 👾 Katia Moran 👾 BlckPhantom 👾 Nick Raker 👾 Otis Morgan 👾 DeezMaster 👾 Treehouse
2021年07月08日
00:00:00 - 07:10:28
Computer Vision and Perception for Self-Driving Cars (Deep Learning Course)

Computer Vision and Perception for Self-Driving Cars (Deep Learning Course)

Learn about Computer Vision and Perception for Self Driving Cars. This series focuses on the different tasks that a Self Driving Car Perception unit would be required to do. ✏️ Course by Robotics with Sakshay. https://www.youtube.com/channel/UC57lEMTXZzXYu_y0FKdW6xA ⭐️ Course Contents and Links ⭐️ ⌨️ (0:00:00) Introduction ⌨️ (0:02:16) Fully Convolutional Network | Road Segmentation 🔗 Kaggle Dataset: https://www.kaggle.com/sakshaymahna/kittiroadsegmentation 🔗 Kaggle Notebook: https://www.kaggle.com/sakshaymahna/fully-convolutional-network 🔗 KITTI Dataset: http://www.cvlibs.net/datasets/kitti/ 🔗 Fully Convolutional Network Paper: https://arxiv.org/abs/1411.4038 🔗 Hand Crafted Road Segmentation: https://www.youtube.com/watch?v=hrin-qTn4L4 🔗 Deep Learning and CNNs: https://www.youtube.com/watch?v=aircAruvnKk ⌨️ (0:20:45) YOLO | 2D Object Detection 🔗 Kaggle Competition/Dataset: https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles 🔗 Visualization Notebook: https://www.kaggle.com/sakshaymahna/lyft-3d-object-detection-eda 🔗 YOLO Notebook: https://www.kaggle.com/sakshaymahna/yolov3-keras-2d-object-detection 🔗 Playlist on Fundamentals of Object Detection: https://www.youtube.com/playlist?list=PL_IHmaMAvkVxdDOBRg2CbcJBq9SY7ZUvs 🔗 Blog on YOLO: https://www.section.io/engineering-education/introduction-to-yolo-algorithm-for-object-detection/ 🔗 YOLO Paper: https://arxiv.org/abs/1506.02640 ⌨️ (0:35:51) Deep SORT | Object Tracking 🔗 Dataset: https://www.kaggle.com/sakshaymahna/kittiroadsegmentation 🔗 Notebook/Code: https://www.kaggle.com/sakshaymahna/deepsort/notebook 🔗 Blog on Deep SORT: https://medium.com/analytics-vidhya/object-tracking-using-deepsort-in-tensorflow-2-ec013a2eeb4f 🔗 Deep SORT Paper: https://arxiv.org/abs/1703.07402 🔗 Kalman Filter: https://www.youtube.com/playlist?list=PLn8PRpmsu08pzi6EMiYnR-076Mh-q3tWr 🔗 Hungarian Algorithm: https://www.geeksforgeeks.org/hungarian-algorithm-assignment-problem-set-1-introduction/ 🔗 Cosine Distance Metric: https://www.machinelearningplus.com/nlp/cosine-similarity/ 🔗 Mahalanobis Distance: https://www.machinelearningplus.com/statistics/mahalanobis-distance/ 🔗 YOLO Algorithm: https://youtu.be/C3qmhPVUXiE ⌨️ (0:52:37) KITTI 3D Data Visualization | Homogenous Transformations 🔗 Dataset: https://www.kaggle.com/garymk/kitti-3d-object-detection-dataset 🔗 Notebook/Code: https://www.kaggle.com/sakshaymahna/lidar-data-visualization/notebook 🔗 LIDAR: https://geoslam.com/what-is-lidar/ 🔗 Tesla doesn't use LIDAR: https://towardsdatascience.com/why-tesla-wont-use-lidar-57c325ae2ed5 ⌨️ (1:06:45) Multi Task Attention Network (MTAN) | Multi Task Learning 🔗 Dataset: https://www.kaggle.com/sakshaymahna/cityscapes-depth-and-segmentation 🔗 Notebook/Code: https://www.kaggle.com/sakshaymahna/mtan-multi-task-attention-network 🔗 Data Visualization: https://www.kaggle.com/sakshaymahna/exploratory-data-analysis 🔗 MTAN Paper: https://arxiv.org/abs/1803.10704 🔗 Blog on Multi Task Learning: https://ruder.io/multi-task/ 🔗 Image Segmentation and FCN: https://youtu.be/U_v0Tovp4XQ ⌨️ (1:20:58) SFA 3D | 3D Object Detection 🔗 Dataset: https://www.kaggle.com/garymk/kitti-3d-object-detection-dataset 🔗 Notebook/Code: https://www.kaggle.com/sakshaymahna/sfa3d 🔗 Data Visualization: https://www.kaggle.com/sakshaymahna/l... 🔗 Data Visualization Video: https://youtu.be/tb1H42kE0eE 🔗 SFA3D GitHub Repository: https://github.com/maudzung/SFA3D 🔗 Feature Pyramid Networks: https://jonathan-hui.medium.com/understanding-feature-pyramid-networks-for-object-detection-fpn-45b227b9106c 🔗 Keypoint Feature Pyramid Network: https://arxiv.org/pdf/2001.03343.pdf 🔗 Heat Maps: https://en.wikipedia.org/wiki/Heat_map 🔗 Focal Loss: https://medium.com/visionwizard/understanding-focal-loss-a-quick-read-b914422913e7 🔗 L1 Loss: https://afteracademy.com/blog/what-are-l1-and-l2-loss-functions 🔗 Balanced L1 Loss: https://paperswithcode.com/method/balanced-l1-loss 🔗 Learning Rate Decay: https://medium.com/analytics-vidhya/learning-rate-decay-and-methods-in-deep-learning-2cee564f910b 🔗 Cosine Annealing: https://paperswithcode.com/method/cosine-annealing ⌨️ (1:40:24) UNetXST | Camera to Bird's Eye View 🔗 Dataset: https://www.kaggle.com/sakshaymahna/semantic-segmentation-bev 🔗 Dataset Visualization: https://www.kaggle.com/sakshaymahna/data-visualization 🔗 Notebook/Code: https://www.kaggle.com/sakshaymahna/unetxst 🔗 UNetXST Paper: https://arxiv.org/pdf/2005.04078.pdf 🔗 UNetXST Github Repository: https://github.com/ika-rwth-aachen/Cam2BEV 🔗 UNet: https://towardsdatascience.com/understanding-semantic-segmentation-with-unet-6be4f42d4b47 🔗 Image Transformations: https://kevinzakka.github.io/2017/01/10/stn-part1/ 🔗 Spatial Transformer Networks: https://kevinzakka.github.io/2017/01/18/stn-part2/
2022年01月27日
00:00:00 - 01:59:38
Week 1 – Lecture: History, motivation, and evolution of Deep Learning

Week 1 – Lecture: History, motivation, and evolution of Deep Learning

Course website: https://bit.ly/DLSP20-web Playlist: http://bit.ly/pDL-YouTube Speaker: Yann LeCun Week 1: http://bit.ly/DLSP20-01 0:00:00 – Week 1 – Lecture LECTURE Part A: http://bit.ly/DLSP20-01-1 We discuss the motivation behind deep learning. We begin with the history and inspiration of deep learning. Then we discuss the history of pattern recognition and introduce gradient descent and its computation by backpropagation. Finally, we discuss the hierarchical representation of the visual cortex. 0:03:37 – Inspiration of Deep Learning and Its History, Supervised Learning 0:24:21 – History of Pattern Recognition and Introduction to Gradient Descent 0:38:56 – Computing Gradients by Backpropagation, Hierarchical Representation of the Visual Cortex LECTURE Part B: http://bit.ly/DLSP20-01-2 We first discuss the evolution of CNNs, from Fukushima to LeCun to Alexnet. We then discuss some applications of CNN's, such as image segmentation, autonomous vehicles, and medical image analysis. We discuss the hierarchical nature of deep networks and the attributes of deep networks that make them advantageous. We conclude with a discussion of generating and learning features/representations. 0:49:25 – Evolution of CNNs 1:05:55 – Deep Learning & Feature Extraction 1:19:27 – Learning Representations #Deep Learning #Yann LeCun #NYU #PyTorch
2020年02月24日
00:00:00 - 01:38:57
Plant Leaf Disease Detection GUI | Machine Learning Projects 1 | Machine Learning Training | Edureka

Plant Leaf Disease Detection GUI | Machine Learning Projects 1 | Machine Learning Training | Edureka

🔥 Edureka Deep Learning Certification training (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎) : https://www.edureka.co/ai-deep-learning-with-tensorflow This Edureka video on ' 𝐏𝐥𝐚𝐧𝐭 𝐋𝐞𝐚𝐟 𝐃𝐢𝐬𝐞𝐚𝐬𝐞 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐆𝐔𝐈' will give you an overview of how to detect the various Plant Leaf Diseases using Image Processing with GUI. Following pointers are covered in this Plant Leaf Disease Detection with GUI : 00:00:00 Agenda 00:00:53 Problem Statement 00:02:26 Tools and Frameworks 00:02:58 Project ------------------------------------ 🔹Checkout Edureka's Machine Learning Project playlist: https://bit.ly/3ij9Uw7 🔹Checkout Edureka's Machine Learning Python Tutorial playlist: https://bit.ly/3szLTCO 🔹Checkout Edureka's Machine Learning R Tutorial Playlist: https://bit.ly/3duYGlF 🔹Checkout Edureka's Machine Learning Tutorial Blog Series: https://bit.ly/2PX5lIp 🔴Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV 📌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/ #edureka #edurekamachinelearning #machinelearning # plantleafdiseasedetectionwithgui #convolutionneuralnetworks #machinelearningpretrainedmodels #machinelearningtutorial #edurekatraining ---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬--------- 🔵 Plant Leaf Disease Detection with GUI: https://bit.ly/36Y6l8g 🔵 House Price Prediction using ML: https://bit.ly/3i0VKzJ 🔵 Emoji Prediction using LSTM: https://bit.ly/2TDuPjR 🔵 Color old photographs using Autoencoders: https://bit.ly/3BQg7r9 🔵 Handwritten Digit Recognition on MNIST dataset: https://bit.ly/3zTCxGf 🔵 Generate Images Using DC-Gan's: https://bit.ly/2TPtYwC ---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧--------- 🔵 Data Science Online Training: https://bit.ly/2NCT239 🟣 Python Online Training: https://bit.ly/2CQYGN7 🔵 AWS Online Training: https://bit.ly/2ZnbW3s 🟣 RPA Online Training: https://bit.ly/2Zd0ac0 🔵 DevOps Online Training: https://bit.ly/2BPwXf0 🟣 Big Data Online Training: https://bit.ly/3g8zksu 🔵 Java Online Training: https://bit.ly/31rxJcY ---------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬--------- 🟣Machine Learning Engineer Masters Program: https://bit.ly/388NXJi 🔵DevOps Engineer Masters Program: https://bit.ly/2B9tZCp 🟣Cloud Architect Masters Program: https://bit.ly/3i9z0eJ 🔵Data Scientist Masters Program: https://bit.ly/2YHaolS 🟣Big Data Architect Masters Program: https://bit.ly/31qrOVv 🔵Business Intelligence Masters Program: https://bit.ly/2BPLtn2 -----------------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏GD 𝐂𝐨𝐮𝐫𝐬𝐞𝐬--------------- 🔵Artificial and Machine Learning PGD: https://bit.ly/2Ziy7b1 -------------------------------------------------------------------- How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate About the Course : Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Why Learn Machine Learning with Python? 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. 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 #agricultural plant leaf disease detection using image processing #plant leaf disease detection using image processing #plant leaf disease detection #plant leaf disease detection with GUI #image processing #CNN for agriculture #deep learning for agriculture #machine learning for agriculture leaf disease detection #ML for leaf detection #DL for leaf detection #disease detection using image processing #deep learning #machine learning #edureka
2021年07月17日
00:00:00 - 00:31:05
Image Classification with Convolutional Neural Networks | Deep Learning with PyTorch: Zero to GANs |

Image Classification with Convolutional Neural Networks | Deep Learning with PyTorch: Zero to GANs |

“Deep Learning with PyTorch: Zero to GANs” is a beginner-friendly online course offering a practical and coding-focused introduction to deep learning using the PyTorch framework. Learn more and register for a certificate of accomplishment here: http://zerotogans.com Watch the entire series here: https://www.youtube.com/playlist?list=PLWKjhJtqVAbm5dir5TLEy2aZQMG7cHEZp Code and Resources: 🔗 Image Classification using Convolutional Neural Networks: https://jovian.ai/aakashns/05-cifar10-cnn 🔗 Classifying images of everyday objects using a neural network: https://jovian.ai/aakashns/03-cifar10-feedforward 🔗 Discussion forum: https://jovian.ai/forum/t/lecture-4-image-classification-with-convolutional-neural-networks/13766 Topics covered in this video: * Working with the 3-channel RGB images from the CIFAR10 dataset * Introduction to Convolutions, kernels & features maps * Underfitting, overfitting, and techniques to improve model performance This course is taught by Aakash N S, co-founder & CEO of Jovian - a data science platform and global community. - YouTube: https://youtube.com/jovianml - Twitter: https://twitter.com/jovianml - LinkedIn: https://linkedin.com/company/jovianml -- 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12月13日
00:00:00 - 02:04:05
What are JSON Web Tokens? JWT Auth Explained [Tutorial]

What are JSON Web Tokens? JWT Auth Explained [Tutorial]

Learn about JWT and how to use them for authentication. JSON Web Tokens are used for representing claims securely between two parties. In this tutorial , you will learn the JWT Auth flow without being distracted by a lot of extra libraries. This tutorial teaches JWT Auth as simply as possible. 💻 Code: https://github.com/weibenfalk/jwtToken-react-express 🎥 Video from Thomas Weibenfalk. Check out his channel: https://www.youtube.com/channel/UCnnnWy4UTYN258FfVGeXBbg -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news #jwt #json web token #jwt tutorial #what is jwt #what is json web token #how does jwt work #jwt authentication #jwt auth #jwt authorization #json web token explained #jwt token #jwt explained #jwt in depth #learn jwt #jwt security #jwt decode #jwt encode #jwt vs session #json web token authentication #json web token tutorial #json web token in depth #using jwt for authentication #how to use jwt #why jwt #why use jwt #jwt node js
2019年11月12日
00:00:00 - 01:41:00
【深層学習】畳み込み層の本当の意味、あなたは説明できますか?【ディープラーニングの世界 vol. 5 】 #057 #VRアカデミア #DeepLearning

【深層学習】畳み込み層の本当の意味、あなたは説明できますか?【ディープラーニングの世界 vol. 5 】 #057 #VRアカデミア #DeepLearning

CNN でおなじみの畳み込み層についての解説です。 幾何的に解釈してやると、かなり意味がわかりやすいと思います。 GitHub はこちら↓ https://github.com/sugiyama34/AIciaSolidProject 動画で用いた Google spreadsheet はこちら↓ https://docs.google.com/spreadsheets/d/1zABw_IwKEOu_4OjEkJAy7jnhEt0ZYRf-ez0A7Mc1RrI/edit?usp=drivesdk 【関連プレイリスト】 Deep Learning の世界 https://www.youtube.com/playlist?list=PLhDAH9aTfnxKXf__soUoAEOrbLAOnVHCP ご視聴ありがとうございました! 良い動画だなと思っていただけたら、高評価、チャンネル登録お願いします! 質問や感想など、気軽にコメントしてくださいね。 【参考文献】 DL4US コンテンツ公開ページ | U-Tokyo Matsuo Lab https://weblab.t.u-tokyo.ac.jp/en/dl4us/ GitHub に公開されているので、 clone してきて、 Google Colaboratory を利用して動かすと、ただで勉強開始できます。 clone, Google colaboratory の利用方法はググりましょう。 DL にはわからないことをググって進める能力も必須です。 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems https://amzn.to/2XNOs6K 英語ですが、これは、基本的な統計から始めていて、基礎を網羅的に扱っている良い教科書だと思います。 (日本語版もあるという噂ですが、それは TF1.0 版らしく、おすすめできません。 どうせ DL やるなら英語読むことにはなるので、挑戦してみても良いかも。) 深層学習 (アスキードワンゴ) https://amzn.to/2AnK0nu The 深層学習の教科書。松尾研の人々が訳したもの。 ゼロから作るDeep Learning https://amzn.to/3eDoRVd DL の framework を自分で作っちゃう本。勉強したし、 DL のモデルは実装したけど、イメージわかない人におすすめ。 深層学習 (機械学習プロフェッショナルシリーズ) https://amzn.to/3eyxNuP 青いあの本。中級~上級者向け。 ディープラーニングと物理学 https://amzn.to/2B9PlPt 理論的な深くて広い世界を探検したい人におすすめ ========= Twitter: https://twitter.com/AIcia_Solid/ Logo: TEICAさん https://twitter.com/T_E_I_C_A Model: http://3d.nicovideo.jp/works/td44519 Model by: W01fa さん https://twitter.com/W01fa Editor: AIris Solid #機械学習 #AIciaSolidProject #VRアカデミア #Vtuber
2020年05月20日
00:00:00 - 00:24:20
Hot Dog or Not Hot Dog – Convolutional Neural Network Course for Beginners

Hot Dog or Not Hot Dog – Convolutional Neural Network Course for Beginners

Learn about Convolutional Neural Networks in this full course for beginners. These are a class of deep learning neural networks that are particularly effective for classifying images. CNNs are also used for other applications such as natural language processing and time series forecasting, but they are most commonly associated with image processing. ✏️ Course developed by @KylieYYing Colab: https://colab.research.google.com/drive/1G_ixTTBy6tVm4R7B7qYEdokjilLBdLdq?usp=sharing Slides: https://docs.google.com/presentation/d/16Z2fnBl2azfGxZ8InHiFvRb1OLZVQTLQpy8GzZR5YEg/edit?usp=sharing Food 101 dataset: https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/ ⭐️ Contents ⭐️ ⌨️ (0:00:00) Intro ⌨️ (0:07:05) Supervised Learning ⌨️ (0:16:54) Training a Model ⌨️ (0:25:58) Neural Nets ⌨️ (0:36:41) Convolutional Neural Nets ⌨️ (0:38:56) Coding Example - Getting Data ⌨️ (1:06:08) Coding Example - Neural Net Implementation ⌨️ (1:14:31) Coding Example - Improvements 🏗 Google provided a grant that made this course possible. Correction: 42:52 Sorry – The code didn't show up right here. Feel free to listen or skip forward to 52:44 where the code shows up again. 🎉 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07月03日
00:00:00 - 01:27:43
Convolutional Neural Network (CNN) | Convolutional Neural Networks With TensorFlow | Edureka

Convolutional Neural Network (CNN) | Convolutional Neural Networks With TensorFlow | Edureka

The code referenced in this video is from https://YouTube.com/Sentdex and https://pythonprogramming.net/convolutional-neural-network-kats-vs-dogs-machine-learning-tutorial/ 🔥 TensorFlow Training (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎") - https://www.edureka.co/ai-deep-learning-with-tensorflow This Edureka "Convolutional Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you in understanding what is Convolutional Neural Network and how it works. It also includes a use-case, in which we will be creating a classifier using TensorFlow. Below are the topics covered in this tutorial: 1. How a Computer Reads an Image? 2. Why can't we use Fully Connected Networks for Image Recognition? 3. What is Convolutional Neural Network? 4. How Convolutional Neural Networks Work? 5. Use-Case (dog and cat classifier) Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE PG in Artificial Intelligence and Machine Learning with NIT Warangal : https://www.edureka.co/post-graduate/machine-learning-and-ai Post Graduate Certification in Data Science with IIT Guwahati - https://www.edureka.co/post-graduate/data-science-program (450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies) - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course. - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. Business Analysts who want to understand Deep Learning (ML) Techniques 4. Information Architects who want to gain expertise in Predictive Analytics 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio. - - - - - - - - - - - - - - Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #yt:cc=on #convolutional neural network #convolutional neural networks #convolutional neural network tutorial #deep learning tutorial #tensorflow tutorial #neural network #neural network tutorial #convolutional neural network tensorflow #convolutional neural network python #convolutional neural network python tensorflow #convolutional neural network tensorflow code #convolutional neural network tensorflow tutorial #convolutional neural network tensorflow example #edureka
2017年09月26日
00:00:00 - 00:22:14
Pythonによるディープラーニングの作り方〜画像認識〜【Python機械学習入門#10】

Pythonによるディープラーニングの作り方〜画像認識〜【Python機械学習入門#10】

いよいよ?深層学習(ディープラーニング)のご紹介です。 世の中のAI(人工知能)の多くに用いられる技術で、画像・音声など数値化しづらいデータを扱う場合にはまず間違いなくDeep learningが使用されます。 この動画では多くの画像認識に使われるCNN(Convolution Neural Network)技術の基本を説明しています。 ライブラリにはtensorflowを使っています。 #データサイエンス
2021年01月19日
00:00:00 - 00:33:55
Deep Learning入門:層数、ニューロン数を決める指針

Deep Learning入門:層数、ニューロン数を決める指針

この動画では、ニューラルネットワークの総数、各層のニューロンの数をどのように決めればよいかについて、そのセオリーを紹介します。 次の動画(ニューラルネットワークの多層化テクニック)はこちらです。 https://www.youtube.com/watch?v=X2KWO1UPqxk 前回の動画(ニューラルネットワーク設計の基礎)はこちらです。 https://www.youtube.com/watch?v=O3qm6qZooP0 再生リスト「Deep Learning入門」 https://www.youtube.com/playlist?list=PLg1wtJlhfh23pjdFv4p8kOBYyTRvzseZ3 Neural Network Console https://dl.sony.com/ja/ Neural Network Libraries https://nnabla.org/ja/ #Deep Learning #Neural Network #Neural Network Console #Neural Network Libraries #Sony #AI #深層学習 #ディープラーニング #ニュールネットワーク #ソニー #人工知能 #ニューロンの数 #層の数 #Layer #Neuron #ノウハウ #畳み込みニューラルネットワーク #Convolutional Neural Networks
2019年04月08日
00:00:00 - 00:07:03
Build a Shopping Cart with React and TypeScript - Tutorial

Build a Shopping Cart with React and TypeScript - Tutorial

Learn the fundamentals and how to build a ReactJS shopping cart with Typescript, Material UI, Styled Components and React-Query. This tutorial uses a free open API for dummy data to the items in the shop. React-Query hooks is used for fetching the data from the API. Styled Components is used in combination with Material UI to customize the styles. 💻 Code: https://github.com/weibenfalk/react-shopping-cart ✏️ Course created by Thomas Weibenfalk. Check out his channel: https://www.youtube.com/channel/UCnnnWy4UTYN258FfVGeXBbg -- 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01月21日
00:00:00 - 01:06:45
How to Build Tetris in React - GameDev Tutorial (with React Hooks!)

How to Build Tetris in React - GameDev Tutorial (with React Hooks!)

Learn to create a Tetris game with React Hooks in this tutorial course for beginners. You will learn how to build Tetris from scratch using hooks like useState, useEffect, useCallback and custom hooks. Styling is done with Styled Components. 💻 Starter files: https://github.com/weibenfalk/react-tetris-starter-files 🎥 Tutorial from Thomas Weibenfalk. Check out his YouTube channel: https://www.youtube.com/channel/UCnnnWy4UTYN258FfVGeXBbg 🔗 Watch more courses from Thomas on his website: https://www.weibenfalk.com/ ⭐️ Course Contents ⭐️ ⌨️ (00:00) Introduction ⌨️ (03:40) create-react-app and tooling ⌨️ (06:57) Scaffolding Components ⌨️ (15:49) Stage and Tetrominos ⌨️ (32:05) Styling with Styled Components ⌨️ (57:19) usePlayer and useStage ⌨️ (1:12:51) Stage update and player movement ⌨️ (1:37:02) Collision Detection ⌨️ (1:50:46) Player RotationG ⌨️ (2:04:12) Clear Rows ⌨️ (2:11:37) drop with useInterval ⌨️ (2:18:47) useGameStatus and React.memo -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://www.freecodecamp.org/news #react #react tutorial #react course #react tutorial for beginners #reactjs #tetris #hooks #React.memo #useEffect #useState #useCallback #programming #web development #development #tech #react hooks #react hooks tutorial #react hooks course
2019年08月15日
00:00:00 - 02:34:18
【深層学習】CNN 実装してみた【ディープラーニングの世界 vol. 7 】 #059 #VRアカデミア #DeepLearning

【深層学習】CNN 実装してみた【ディープラーニングの世界 vol. 7 】 #059 #VRアカデミア #DeepLearning

CNN を実装し、 Dense layer のみのものとの性能比較をしました。 CNN がいかに画像 Deep 向けかを実感できる結果となりました! GitHub はこちら↓ https://github.com/sugiyama34/AIciaSolidProject ご視聴ありがとうございました! 良い動画だなと思っていただけたら、高評価、チャンネル登録お願いします! 質問や感想など、気軽にコメントしてくださいね。 =================================== 【関連プレイリスト】 Deep Learning の世界 https://www.youtube.com/playlist?list=PLhDAH9aTfnxKXf__soUoAEOrbLAOnVHCP 【参考文献】 DL4US コンテンツ公開ページ | U-Tokyo Matsuo Lab https://weblab.t.u-tokyo.ac.jp/en/dl4us/ GitHub に公開されているので、 clone してきて、 Google Colaboratory を利用して動かすと、ただで勉強開始できます。 clone, Google colaboratory の利用方法はググりましょう。 DL にはわからないことをググって進める能力も必須です。 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems https://amzn.to/2XNOs6K 英語ですが、これは、基本的な統計から始めていて、基礎を網羅的に扱っている良い教科書だと思います。 (日本語版もあるという噂ですが、それは TF1.0 版らしく、おすすめできません。どうせ DL やるなら英語読むことにはなるので、挑戦してみても良いかも。) 深層学習 (アスキードワンゴ) https://amzn.to/2AnK0nu The 深層学習の教科書。松尾研の人々が訳したもの。 ゼロから作るDeep Learning https://amzn.to/3eDoRVd DL の framework を自分で作っちゃう本。勉強したし、 DL のモデルは実装したけど、イメージわかない人におすすめ。 深層学習 (機械学習プロフェッショナルシリーズ) https://amzn.to/3eyxNuP 青いあの本。中級~上級者向け。 ディープラーニングと物理学 https://amzn.to/2B9PlPt 理論的な深くて広い世界を探検したい人におすすめ =================================== Twitter: https://twitter.com/AIcia_Solid/ Logo: TEICAさん https://twitter.com/T_E_I_C_A Model: http://3d.nicovideo.jp/works/td44519 Model by: W01fa さん https://twitter.com/W01fa Editor: AIris Solid ( https://twitter.com/AIris_Solid/ )
2020年06月05日
00:00:00 - 00:09:25
Fuzzy Logic in Artificial Intelligence | Introduction to Fuzzy Logic & Membership Function | Edureka

Fuzzy Logic in Artificial Intelligence | Introduction to Fuzzy Logic & Membership Function | Edureka

***AI and Deep Learning using TensorFlow: https://www.edureka.co/ai-deep-learning-with-tensorflow *** This Edureka Live video on "Fuzzy Logic in AI" will explain what is fuzzy logic and how it is used to find different possibilities between 0 and 1. It also explains the architecture of this logic along with real-time examples. (blog: https://www.edureka.co/blog/fuzzy-logic-ai/ ) ----------------------------------------------------------- Machine Learning Podcast - http://bit.ly/2IGLYCc Complete Youtube Playlist here: https://bit.ly/2OhZEpz Deep Learning Blog Series: https://bit.ly/2xVIMe1 Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning/ Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #edureka #edurekadeeplearning #tensorflow #FuzzyLogic #FuzzyLogicinAI About the course: Edureka's Deep Learning in TensorFlow with Python Certification Training is curated by industry professionals as per the industry requirements & demands. You will master the concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM) and work with libraries like Keras & TFLearn. The course has been specially curated by industry experts with real-time case studies. ------------------------------------------------------ Objectives: Deep Learning in TensorFlow with Python Training is designed by industry experts to make you a Certified Deep Learning Engineer. The Deep Learning in TensorFlow course offers: In-depth knowledge of Deep Neural Networks Comprehensive knowledge of various Neural Network architectures such as Convolutional Neural Network, Recurrent Neural Network, Autoencoders Implementation of Collaborative Filtering with RBM The exposure to real-life industry-based projects which will be executed using TensorFlow library Rigorous involvement of an SME throughout the AI & Deep Learning Training to learn industry standards and best practices ------------------------------------------------- Why should one go for this course? Deep Learning is one of the most accelerating and promising fields, among all the technologies available in the IT market today. To become an expert in this technology, you need structured training with the latest skills as per current industry requirements and best practices. Besides strong theoretical understanding, you will be working on various real-life data projects using different neural network architectures as a part of the solution strategy. Additionally, you will receive guidance from a Deep Learning expert who is currently working in the industry on real-life projects. --------------------------------------------------- Skills that you will be learning: Deep Learning in TensorFlow with Python Training will help you to become a Deep Learning Engineer. It will hone your skills by offering you comprehensive knowledge on Deep Learning in TensorFlow. It will also acquaint you with the required hands-on experience for solving real-time industry-based Deep Learning projects. During this course you will be trained by our expert instructors on: Deep Learning and TensorFlow Concepts Working with Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) Proficiency in Long short-term memory (LSTM) Implementing Keras, TFlearn, Autoencoders Implementing Restricted Boltzmann Machine (RBM) Knowledge of Neural Networks & Natural Language Processing (NLP) Using Python with TensorFlow Libraries Perform Text Analytics Perform Text Processing -------------------------------------------------- Who should go for this course? The TensorFlow with Python Training is for all the professionals who are passionate about Deep Learning and want to go ahead and make their career as a Deep Learning Engineer. It is best suited for individuals who are: Developers aspiring to be a 'Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Deep Learning (ML) Techniques Information Architects who want to gain expertise in Predictive Analytics Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused on one industry or skill set, it can be used by anyone to enhance their portfolio. ------------------------------------------------ If you are looking for live online training, write back to us at [email protected] or call us at the US: + 18338555775 (Toll-Free) or India: +91 9606058406 for more information. #yt:cc=on #fuzzy logic #fuzzy logic in ai #fuzzy logic in artificial intelligence #fuzzy set #fuzzy in ai #what is fuzzy logic #what is fuzzy logic in AI #what is fuzzy logic in artificial intelligence #fuzzy logic vs probability #artificial intelligence #artificial intelligence tutorial #fuzzy in artificial intelligence #what is fuzzy set #fuzzy logic basics #fuzzy logic tutorial #fuzzy sets and fuzzy logic #fuzzy set example #artificial intelligence edureka #edureka
2019年12月18日
00:00:00 - 00:19:28
【深層学習】プーリング層 - シンプルだけど大きな役割を担う層【ディープラーニングの世界 vol. 6 】 #058 #VRアカデミア #DeepLearning

【深層学習】プーリング層 - シンプルだけど大きな役割を担う層【ディープラーニングの世界 vol. 6 】 #058 #VRアカデミア #DeepLearning

CNN でよく使われる Pooling Layer の説明です。 シンプルですが、意外といろんな役割を担ってくれています。 多くの課題を解決する、シンプルな方法が一番いいですよね! GitHub はこちら↓ https://github.com/sugiyama34/AIciaSolidProject 動画で用いた Google spreadsheet はこちら↓ https://docs.google.com/spreadsheets/d/1zABw_IwKEOu_4OjEkJAy7jnhEt0ZYRf-ez0A7Mc1RrI/edit?usp=drivesdk 【関連プレイリスト】 Deep Learning の世界 https://www.youtube.com/playlist?list=PLhDAH9aTfnxKXf__soUoAEOrbLAOnVHCP ご視聴ありがとうございました! 良い動画だなと思っていただけたら、高評価、チャンネル登録お願いします! 質問や感想など、気軽にコメントしてくださいね。 【参考文献】 DL4US コンテンツ公開ページ | U-Tokyo Matsuo Lab https://weblab.t.u-tokyo.ac.jp/en/dl4us/ GitHub に公開されているので、 clone してきて、 Google Colaboratory を利用して動かすと、ただで勉強開始できます。 clone, Google colaboratory の利用方法はググりましょう。 DL にはわからないことをググって進める能力も必須です。 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems https://amzn.to/2XNOs6K 英語ですが、これは、基本的な統計から始めていて、基礎を網羅的に扱っている良い教科書だと思います。 (日本語版もあるという噂ですが、それは TF1.0 版らしく、おすすめできません。 どうせ DL やるなら英語読むことにはなるので、挑戦してみても良いかも。) 深層学習 (アスキードワンゴ) https://amzn.to/2AnK0nu The 深層学習の教科書。松尾研の人々が訳したもの。 ゼロから作るDeep Learning https://amzn.to/3eDoRVd DL の framework を自分で作っちゃう本。勉強したし、 DL のモデルは実装したけど、イメージわかない人におすすめ。 深層学習 (機械学習プロフェッショナルシリーズ) https://amzn.to/3eyxNuP 青いあの本。中級~上級者向け。 ディープラーニングと物理学 https://amzn.to/2B9PlPt 理論的な深くて広い世界を探検したい人におすすめ ========= Twitter: https://twitter.com/AIcia_Solid/ Logo: TEICAさん https://twitter.com/T_E_I_C_A Model: http://3d.nicovideo.jp/works/td44519 Model by: W01fa さん https://twitter.com/W01fa Editor: AIris Solid
2020年05月29日
00:00:00 - 00:10:00
Deep Learning Interview Questions and Answers | AI & Deep Learning Interview Questions | Edureka

Deep Learning Interview Questions and Answers | AI & Deep Learning Interview Questions | Edureka

** AI and Deep-Learning with TensorFlow - https://www.edureka.co/ai-deep-learning-with-tensorflow ** This video covers most of the hottest deep learning interview questions and answers. It also provides you with an understanding process of Deep Learning and the various aspects of it. PG in Artificial Intelligence and Machine Learning with NIT Warangal : https://www.edureka.co/post-graduate/machine-learning-and-ai Post Graduate Certification in Data Science with IIT Guwahati - https://www.edureka.co/post-graduate/data-science-program (450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies) #edureka #DeepLearningInterviewQuestions #TensorFlowInterviewQuestions #DeepLearning #TensorFlow ------------------------------------------------- *** Machine Learning Podcast - https://castbox.fm/channel/id1832236 *** Instagram: https://www.instagram.com/edureka_learning Slideshare: https://www.slideshare.net/EdurekaIN/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka -------------------------------------------------- About the course: Edureka's Deep Learning in TensorFlow with Python Certification Training is curated by industry professionals as per the industry requirements & demands. You will master the concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM) and work with libraries like Keras & TFLearn. The course has been specially curated by industry experts with real-time case studies. ------------------------------------------------------ Objectives: Deep Learning in TensorFlow with Python Training is designed by industry experts to make you a Certified Deep Learning Engineer. The Deep Learning in TensorFlow course offers: In-depth knowledge of Deep Neural Networks Comprehensive knowledge of various Neural Network architectures such as Convolutional Neural Network, Recurrent Neural Network, Autoencoders Implementation of Collaborative Filtering with RBM The exposure to real-life industry-based projects which will be executed using TensorFlow library Rigorous involvement of an SME throughout the AI & Deep Learning Training to learn industry standards and best practices ------------------------------------------------- Why should one go for this course? Deep Learning is one of the most accelerating and promising fields, among all the technologies available in the IT market today. To become an expert in this technology, you need structured training with the latest skills as per current industry requirements and best practices. Besides strong theoretical understanding, you will be working on various real-life data projects using different neural network architectures as a part of the solution strategy. Additionally, you will receive guidance from a Deep Learning expert who is currently working in the industry on real-life projects. --------------------------------------------------- Skills that you will be learning: Deep Learning in TensorFlow with Python Training will help you to become a Deep Learning Engineer. It will hone your skills by offering you comprehensive knowledge on Deep Learning in TensorFlow. It will also acquaint you with the required hands-on experience for solving real-time industry-based Deep Learning projects. During this course you will be trained by our expert instructors on: Deep Learning and TensorFlow Concepts Working with Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) Proficiency in Long short-term memory (LSTM) Implementing Keras, TFlearn, Autoencoders Implementing Restricted Boltzmann Machine (RBM) Knowledge of Neural Networks & Natural Language Processing (NLP) Using Python with TensorFlow Libraries Perform Text Analytics Perform Text Processing -------------------------------------------------- Who should go for this course? The TensorFlow with Python Training is for all the professionals who are passionate about Deep Learning and want to go ahead and make their career as a Deep Learning Engineer. It is best suited for individuals who are: Developers aspiring to be a 'Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Deep Learning (ML) Techniques Information Architects who want to gain expertise in Predictive Analytics Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused on one industry or skill set, it can be used by anyone to enhance their portfolio. ------------------------------------------------ For more information, Please write back to us at [email protected] or call us at: IND: 9606058406 / US: 18338555775 (toll free) #yt:cc=on #deep learning interview questions #Deep Learning Interview Questions and Answers #AI & Deep Learning Interview Questions #tensorflow interview questions #deep learning interview preparation #deep learning interview #AI interview Questions #AI interview Questions and amnswers #data science interview questions and answers #data science interview questions #macine learning interview questions #deep learning #tensorflow #deep learning training #tensorflow training #edureka
2019年04月19日
00:00:00 - 00:40:42