Learn PyTorch for deep learning in a day. Literally.

Welcome to the most beginner-friendly place on the internet to learn PyTorch for deep learning.

Below are the timestamps/outline of the video. The video you're watching is comprised of 162 smaller videos but YouTube limits timestamps at 100 so some have been left out.

00:00 Hello :)

🛠 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
9:56:45 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

#pytorch #machinelearning #deeplearning

[mrdbourke]

Who's ready to code some fire???? 🔥

Pun intended ;)

Welcome to the world of machine learning my friend.

It's a fun place.

---

PS Don't forget to take breaks.

Practice, break, practice, break. Enjoy both.

Because much of learning happens when you're not doing anything, walking around, or taking a nap. And two ideas in your head collide.


Consider this video a momentum builder.

[gg__]

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.

[Dim-ztei]

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

[dormansutt]

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

[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.

[mostafaalkady]

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!

[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

[fr]

One of the best ML/PyTorch videos I’ve ever come across. Straight forward and packed with need to know information. We need more people like this in tech!

[deepaksingh]

Totally loving it .
loved the content, the simplicity the course is designed, and the way the course content is briefed is just perfect.

[amandachang]

This is amazing and honestly addicting to watch/practice with. Thank you so much for all the effort you've clearly put into this; I LOVE this tutorial and you're a great teacher.

[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

[mswiseman]

This is beyond incredible. I really appreciate you breaking everything down and providing outside resources.

[adamvo]

Finished it all!
Daniel, you are a true legend.
Your teaching style is awesome and your enthusiasm is addictive.
Thank you so much for helping so many people get started on their ML journey.

[muzammilomarzoy]

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

[farisrustom]

Fantastic video Daniel! Just finished the video after ~2 weeks and I can honestly say this is the best free educational resource I have come across, thank you so much!

[mgwyndolin]

Great content great presentation! You have the rare and amazing ability to arrive at the very essence of things before explaining them with an extreme clarity. I can never thank you enough for all the effort put into making this.

[yashjindal]

Excited to complete this all the way! Thank you for this amazing course Daniel
Will come back when I finish all the modules here

[TheLegend-muzg]

Finished the video after a couple weeks, doing around an hour of the video per day. Seriously this was great and was exactly what I was looking for. big thanks!

[allcoolandnew]

Hi Daniel, wanted to make sure to thank you for sharing these great videos. You've been a big part of my daily routine for the last couple of weeks :) I feel that the fact that you've learned many of these things relatively recently yourself gives you a real edge because you still have fresh in mind the concepts you might've struggled with yourself. And your teaching style of repetition and positivity (I'm thinking: "Wow my internet connection is slow, but that's OK, it gives us more time to think about what we're learning here" 😄) really make the whole process fun and approachable. You're a great teacher! Expecting to see you in the bestsellers lists soon ;) Grts from the Netherlands

[mnh]

Finally completed the course!! Thanks for your work Daniel! It was a really fun journey to learn deep learning with Pytorch