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.

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
9:57:13 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

[tammodirksen]

The times we live in: Getting 20+ quality hours of Machine Leaning and Deep Learning for free just one click away. How awesome is that.

[umer-un]

Just finished this course, broke it down took well over a month and a half but totally worth it. The way Daniel builds momentum and make you practice it again and again is just amazing, the curriculum is fantastic, This is pure gold.

[ojyltfb]

Thanks, guy. For providing this course as a totally free one. I am looking forward to see a complete pytorch tutorial for a long time. You saved my time. Thank you again.

[SureshSubramaniam-mjuq]

Amazing tutorial! Really explores the experimental methods of ML and ways to improve your model. And it starts from the basics, assumes nothing, and takes to you to a level where you can further progress by yourself.

[arno_roeder]

Thanks for the awesome PyTorch introduction!

[madhumitakumari]

2:12:52 tensor attributes 2:27:00 Matrix Multiplication_1, 2:48:00 Matrix multiplication

[atomicCache_]

It took me almost a month to wrap up this PyTorch tutorial, but hey, it was totally worth the time! Daniel did an awesome job explaining everything in a way that made sense.😊😊

[rohanaggarwal]

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

[lephu]

After 13 days of learning, I have finally completed the course. A special thank you to Mr. Bourke for creating a great course. You are a fantastic teacher, and I hope the knowledge you imparted will help me a lot in my self-study journey to become a skilled data scientist or machine learning engineer in the future.
Day 1: 1:10:00
Day 2: 2:53:03
Day 3: 3:23:28
Day 4 : 4:17:22
Day 5 : 6:02:27
Day 6 : 07:02:33
Day 7+8 : 10:57:58
Day 9: 14:00:47
Day 10: 15:39:43
Day 11: 19:37:54
Day 12: 21:38:22
Day 13 : Finished

[arunyboy]

(In case anybody had this question from the linear regression model) ~ 6:49:00
How does the loss function keep track of which model it is for? This question stems from loss_fn itself is not defined in a way that conects it to a model(e.g. nn.MSELoss) and also loss itself is calculated only on training ourputs and predicted outputs. (loss = loss_fn(y_pred, y_train)).

In PyTorch, whenever you perform operations on tensors (like a forward pass through a model), a computational graph is created. This graph records the operations and the relationships between tensors (like which tensors were used to create a new tensor). In this graph, tensors are nodes, and operations are edges. When you do a forward pass, say y_pred = model_0(X_train), it creates a part of the graph where y_pred is connected to the parameters of model_0 and the input X_train.

When you call loss.backward(), PyTorch traverses this graph backward from the loss node. It calculates the gradient of loss with respect to each tensor that has requires_grad=True.If loss was calculated from outputs of model_0, the computational graph links the loss to model_0's parameters. Therefore, loss.backward() computes gradients for model_0's parameters.

[anby]

Finally completed the whole video, after a month. Absolutely amazing stuff. Daniel bourke is an amazing teacher.

[kcmtvod]

Selective learning if you have less time
0:43:18 7. What is/why PyTorch?
0:53:33 8. What are tensors?
1:14:28 12. Getting setup
1:22:08 13. Introduction to tensors
1:35:35 14. Creating tensors
2:03:26 18. Tensor attributes (information about tensors)
2:11:50 19. Manipulating tensors
2:17:50 20. Matrix multiplication
3:23:28 27. Selecting data (indexing)
4:17:27 33. Introduction to PyTorch Workflow
4:37:12 36. Creating training and test sets (the most important concept in ML)
5:49:31 44. Setting up a loss function and optimizer
6:02:24 45. PyTorch training loop intuition
6:49:31 49. Writing testing loop code
7:44:28 54. Putting everything together
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. Turning 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

[platanus]

I'm planning to learn the 25 hours course within 4 weeks.
2023-08-01: 1:14:30
2023-08-02: 4:12:33
2023-08-03: 4:45:33
2023-08-08 8:13:32
2023-08-09 13:57:46
2023-08-10 17:13:36
2023-08-14 19:26:30
2023-08-15 21:43:24
2023-08-16 24:00:00
2023-08-17 Completed
Thank you Daniel for this great course!

[jasonzhang]

Thanks, Daniel! Really loving this series, and your voice has such an uplifting quality to it – totally a fan of that.

[dmtrung]

3:50:15 I think the problem with the seed is not it only work once, but the random function will keep the last calculated value as the seed, (which is how with the default seed they manage to have different random results every time). Therefore, calling manual_seed again simply reset the seed, as well as the function to the desired values

[aminlee]

i can't express my feelings using words you guys are doing an excellent job for students like us am machine learning student i just want to say Thank you ! keep up the good work

[guydebyl]

wow.. and not just a course.... it is an extraordinary course.. so well laid out... even for a beginner.. congratulations to the authour :)

[qmcsboi]

Hands down One of The Best Tutorials for ML and DL and PyTorch and Python all smushed into one big video. Hats Off . Amazing . Thank you for this gem of a class.

[Abdurahman-rpqn]

Your first hour, of course, just shakes off all of our doubts about the choice of a framework, I appreciate it

[bwallace]

A brilliant educator. The style of delivery suits me incredibly well. Thank you freeCodeCamp and the talented Mr. Bourke!