PyTorch for Deep Learning & Machine Learning – Full Course
Skills:
Supervised Learning90%Unsupervised Learning80%ML Maths Basics70%CV Basics60%LLM Foundations50%
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. Disc
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Chapters (34)
Introduction
1:45
0. Welcome and "what is deep learning?"
7:41
1. Why use machine/deep learning?
11:15
2. The number one rule of ML
16:55
3. Machine learning vs deep learning
23:02
4. Anatomy of neural networks
32:24
5. Different learning paradigms
36:56
6. What can deep learning be used for?
43:18
7. What is/why PyTorch?
53:33
8. What are tensors?
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
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. Disc
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