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.
All code on GitHub - https://dbourke.link/pt-github
Ask a question - https://dbourke.link/pt-github-discussions
Read the course materials online - https://learnpytorch.io
Sign up for the full course on Zero to Mastery (20+ hours more video) - https://dbourke.link/ZTMPyTorch
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. Ma
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Chapters (36)
Hello :)
1:17
0. Welcome and "what is deep learning?"
7:13
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
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. Ma
🎓
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