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Learn Applied Machine Learning through PyTorch, course taught by Daniel Bourke

Home Page:https://www.youtube.com/watch?v=V_xro1bcAuA&t=26s

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Learn PyTorch in ~25hours

Beginner steps to learn pytorch.
    Course by Daniel Bourke, posted by freecodecamp.org

References:

Course Progress

πŸ›  Chapter 0 – PyTorch Fundamentals

  • βœ…0:01:45 0. Welcome and "what is deep learning?"
  • βœ…0:07:41 1. Why use machine/deep learning?
  • βœ…0:11:15 2. The number one rule of ML
  • βœ…0:16:55 3. Machine learning vs deep learning
  • βœ…0:23:02 4. Anatomy of neural networks
  • βœ…0:32:24 5. Different learning paradigms
  • βœ…0:36:56 6. What can deep learning be used for?
  • βœ…0:43:18 7. What is/why PyTorch?
  • βœ…0:53:33 8. What are tensors?
  • βœ…0:57:52 9. Outline
  • βœ…1:03:56 10. How to (and how not to) approach this course
  • βœ…1:09:05 11. Important resources
  • βœ…1:14:28 12. Getting setup
  • βœ…1:22:08 13. Introduction to tensors
  • βœ…1:35:35 14. Creating tensors
  • βœ…1:54:01 17. Tensor datatypes
  • βœ…2:03:26 18. Tensor attributes (information about tensors)
  • βœ…2:11:50 19. Manipulating tensors
  • βœ…2:17:50 20. Matrix multiplication
  • βœ…2:48:18 23. Finding the min, max, mean & sum
  • βœ…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

πŸ—Ί 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

πŸ—ƒ 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

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Learn Applied Machine Learning through PyTorch, course taught by Daniel Bourke

https://www.youtube.com/watch?v=V_xro1bcAuA&t=26s


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