@muzammilomarzoy6616
Day 1 | 31:56 5. Different learning paradigms Day 2 | 1:21:40 13. Introduction to tensors Day 3 | 1:53:33 17. Tensor datatypes Day 4 | 2:17:22 20. Matrix multiplication Day 5 | 3:11:03 26. Squeezing, unsqueezing and permuting Day 6 | 4:16:59 33. Introduction to PyTorch Workflow
@Dim-zt5ei
Thank you, Daniel. I completed the 25 hours. I'm happy to see people putting in such great efforts to make knowledge accessible for free
@gg_505_
We live in a time where you can have this level of information for free. You are awesome dude, thanks for putting this up! I'll comment again once I finish the course.
@teidenzero
Day 4, I'm halfway through and I'm looking at code that looked alien to me 5 days ago and it starts to make sense. Absolute beast of a course.
@mostafaalkady6556
This gentleman was generous with his time and effort to teach people for free! I'm following the course now and it's very easy and I'm enjoying every bit of it. We need more people like you in the world!
@amortalbeing
🛠 Chapter 0: PyTorch Fundamentals 01:17 0. Welcome and "what is deep learning?" 07:13 1. Why use machine/deep learning? 10:47 2. The number one rule of ML 16:27 3. Machine learning vs deep learning 22:34 4. Anatomy of neural networks 31:56 5. Different learning paradigms 36:28 6. What can deep learning be used for? 42:50 7. What is/why PyTorch? 53:05 8. What are tensors? 57:24 9. Outline 1:03:28 10. How to (and how not to) approach this course 1:08:37 11. Important resources 1:14:00 12. Getting setup 1:21:40 13. Introduction to tensors 1:35:07 14. Creating tensors 1:53:33 17. Tensor datatypes 2:02:58 18. Tensor attributes (information about tensors) 2:11:22 19. Manipulating tensors 2:17:22 20. Matrix multiplication 2:47:50 23. Finding the min, max, mean and sum 2:57:20 25. Reshaping, viewing and stacking 3:11:03 26. Squeezing, unsqueezing and permuting 3:23:00 27. Selecting data (indexing) 3:32:33 28. PyTorch and NumPy 3:41:42 29. Reproducibility 3:52:30 30. Accessing a GPU 4:04:21 31. Setting up device agnostic code 🗺 Chapter 1: PyTorch Workflow 4:16:59 33. Introduction to PyTorch Workflow 4:19:46 34. Getting setup 4:27:02 35. Creating a dataset with linear regression 4:36:44 36. Creating training and test sets (the most important concept in ML) 4:52:50 38. Creating our first PyTorch model 5:13:13 40. Discussing important model building classes 5:19:41 41. Checking out the internals of our model 5:29:33 42. Making predictions with our model 5:40:47 43. Training a model with PyTorch (intuition building) 5:49:03 44. Setting up a loss function and optimizer 6:01:56 45. PyTorch training loop intuition 6:39:37 48. Running our training loop epoch by epoch 6:49:03 49. Writing testing loop code 7:15:25 51. Saving/loading a model 7:44:00 54. Putting everything together 🤨 Chapter 2: Neural Network Classification 8:31:32 60. Introduction to machine learning classification 8:41:14 61. Classification input and outputs 8:50:22 62. Architecture of a classification neural network 9:09:13 64. Turing our data into tensors 9:25:30 66. Coding a neural network for classification data 9:43:27 68. Using torch.nn.Sequential 9:56:45 69. Loss, optimizer and evaluation functions for classification 10:11:37 70. From model logits to prediction probabilities to prediction labels 10:27:45 71. Train and test loops 10:57:27 73. Discussing options to improve a model 11:27:24 76. Creating a straight line dataset 11:45:34 78. Evaluating our model's predictions 11:50:58 79. The missing piece: non-linearity 12:42:04 84. Putting it all together with a multiclass problem 13:23:41 88. Troubleshooting a mutli-class model 😎 Chapter 3: Computer Vision 14:00:20 92. Introduction to computer vision 14:12:08 93. Computer vision input and outputs 14:22:18 94. What is a convolutional neural network? 14:27:21 95. TorchVision 14:36:42 96. Getting a computer vision dataset 15:01:06 98. Mini-batches 15:08:24 99. Creating DataLoaders 15:51:33 103. Training and testing loops for batched data 16:25:59 105. Running experiments on the GPU 16:29:46 106. Creating a model with non-linear functions 16:41:55 108. Creating a train/test loop 17:13:04 112. Convolutional neural networks (overview) 17:21:29 113. Coding a CNN 17:41:18 114. Breaking down nn.Conv2d/nn.MaxPool2d 18:28:34 118. Training our first CNN 18:43:54 120. Making predictions on random test samples 18:55:33 121. Plotting our best model predictions 19:19:06 123. Evaluating model predictions with a confusion matrix 🗃 Chapter 4: Custom Datasets 19:43:37 126. Introduction to custom datasets 19:59:26 128. Downloading a custom dataset of pizza, steak and sushi images 20:13:31 129. Becoming one with the data 20:38:43 132. Turning images into tensors 21:15:48 136. Creating image DataLoaders 21:24:52 137. Creating a custom dataset class (overview) 21:42:01 139. Writing a custom dataset class from scratch 22:21:22 142. Turning custom datasets into DataLoaders 22:28:22 143. Data augmentation 22:42:46 144. Building a baseline model 23:10:39 147. Getting a summary of our model with torchinfo 23:17:18 148. Creating training and testing loop functions 23:50:31 151. Plotting model 0 loss curves 23:59:34 152. Overfitting and underfitting 24:32:03 155. Plotting model 1 loss curves 24:35:25 156. Plotting all the loss curves 24:46:22 157. Predicting on custom data yes its available in the description, but its for ease of use ! Thanks mrdbourke
@shifty89
One of the best (and the longest) instructional videos I've ever seen (still working through it). Thanks for this tremendous, free effort.
@wengti0608
Day 1: 1:03:28 10. How to (and how not to) approach this course (11/09/2024) Day 2: 1:35:07 14. Creating tensors (12/09/2024) Day 3: 3:32:33 28. PyTorch and NumPy (13/09/2024) Day 4: 4:16:59 33. Introduction to PyTorch Workflow (14/09/2024) Day 5: 6:15:48 45. PyTorch training loop intuition (15/09/2024) Day 6: 8:31:32 60. Introduction to machine learning classification (16/09/2024) Day 7: 10:27:45 71. Train and test loops (17/09/2024) Day 8: 11:50:58 79. The missing piece: non-linearity (18/09/2024) Day 9: 14:00:20 92. Introduction to computer vision (19/09/2024) Day 10: 16:00:08 103. Training and testing loops for batched data (20/09/2024) Day 11: 17:46:02 114. Breaking down nn.Conv2d/nn.MaxPool2d (21/09/2024) Day 12: 18:35:34 118. Training our first CNN (22/09/2024) Day 13: 19:43:37 126. Introduction to custom datasets (23/09/2024) Day 14: 20:33:00 129. Becoming one with the data (24/09/2024) Day 15: 21:30:30 137. Creating a custom dataset class (overview) (25/09/2024) Day 16: 23:51:03 151. Plotting model 0 loss curves (26/09/2024) Day 17: Completed (27/09/2024) Took me 17 days to complete these lessons. I am planning to continue learning the rest of the content over on Udemy. Thank you, Daniel for creating such amazing free content. This is, by far, the best online coding tutorials I have ever participated in!
@dormansutt8429
A few people said they didn't like that you keep repeating things, but I really appreciate you doing that because for deep learning is a very complicated subject, so you repeating stuff really helps me a lot. btw thanks for the course, Daniel
@dBanzy
What a time to be alive 25 hours formation on PyTorch, I didn't even care about deep learning but now I want to create things with this new-found knowledge
@karunyaronith2404
Commenting to track my progress daily: Day 1: 42:50 What is/why PyTorch? Day 2: 2:02:58 Tensor attributes (information about tensors) Day 3: 4:04:21 Setting up device agnostic code Day 4: 5:19:41 Checking out the internals of our model Day 5: 7:15:25 Saving/loading a model
@kegklaus5069
Just finished the 26-giant-hours machine learning course, and I learned so much! Thanks you so much!
@deepaksingh9318
Totally loving it . loved the content, the simplicity the course is designed , and the way the course content is briefed is just perfect.
@angelmanuelvelascoreyes6741
the best Pytorch course i have ever seen , thank you, i had to spend a long time trying to find a place to learn step by step and an interactive way as you have made your classes
@arnaudtremblay2243
PyTorch lets you call zero_grad() manually because it is sometimes useful to store gradients before deleting them. An exemple would be a recurrent neural network (RNN).
@tsukuruuu
Day 1: 3:23:01 Nov 6 2023 Day 2: exercise + 5:13:12 Nov 7 Day 3 : 8:31:02 + revised SGD, Batch GD , Backpropagation Nov 8 Day 4: 14:00:00 (have to do leetcode and sql more tomorrow) Nov 9 Day 5: 17:13:31 (will learn CNN and CIFAR dataset project tomorrow) Nov 10 Day 6: 17:41:25 ( didn't feel like doing anything) Nov 11 Day 7: 1:01:36:57 (finished) Nov 12 not done yet! much to go! will finish the whole course now then move on to learning and understanding missing theory parts and building projects
@starshipx1282
You're a natural. I previously did the full tensorflow course and this is just another "blessing" of yours if I may say. Will be going with the full course on zm.
@philq01
Thanks! Here's an ode a lonely tensor: A lonely tensor, floating through space, No one to share its calculations, no friendly face. It stretches and bends, calculating alone, Yearning for a companion, a partner of its own. It performs its tasks with diligence and care, But all the while, a deep loneliness it can't bare. It longs for connection, a bond that's true, A fellow tensor, to share its math with, new. But until then, it'll continue on its way, A solitary being, but still beautiful, in its own unique way.
@hannanshaikh5939
Thirteen Days completion results: 2:07:53 Day 1 (January 17) 3:57:31 Day 2 (January 18) 4:57:37 + exercises Day 3 (January 19) [was out for the whole day] 6:19:37 Day 4 (January 20) [took time to understand the codes cuz I am not a computer science student] 9:00:00 Day 5 (January 21) 10:35:27 Day 6 (January 22) :( 12:30:00 Day 7 (January 23) :/ 14:48:28 Day 8 (January 24) :\ 16:38:00 Day 9 (January 25) :| 18:26:02 Day 10 (January 26) :| 21:22:34 Day 11 (January 27) :] 24:10:24 Day 12 (January 28) :) 1:01:36:57 Day 13 (January 29) :) Completed Finally!!! A lot still remains......
@mrdbourke