Deep Learning with PyTorch

  1. Data Loading and Processing
    1. The torch.utils.data Module
      1. Data Loading Architecture
        1. Dataset and DataLoader Relationship
        2. Dataset Classes
          1. Abstract Dataset Class
            1. Map-style Datasets
              1. Implementing len()
                1. Implementing getitem()
                2. Iterable-style Datasets
                  1. Implementing iter()
                  2. Custom Dataset Implementation
                    1. File-based Datasets
                      1. In-memory Datasets
                        1. Lazy Loading Strategies
                      2. DataLoader Class
                        1. Creating DataLoaders
                          1. Basic DataLoader Setup
                            1. Batch Size Configuration
                            2. Data Shuffling
                              1. Random Shuffling
                                1. Deterministic Shuffling
                                2. Parallel Data Loading
                                  1. num_workers Parameter
                                    1. Multiprocessing Considerations
                                    2. Memory Management
                                      1. pin_memory Option
                                        1. Memory Mapping
                                        2. Sampling Strategies
                                          1. Random Sampling
                                            1. Weighted Sampling
                                              1. Stratified Sampling
                                            2. Built-in Datasets
                                              1. TorchVision Datasets
                                                1. MNIST
                                                  1. CIFAR-10 and CIFAR-100
                                                    1. ImageNet
                                                      1. COCO
                                                      2. TorchText Datasets
                                                        1. IMDB Reviews
                                                          1. AG News
                                                            1. Multi30k
                                                            2. TorchAudio Datasets
                                                              1. LibriSpeech
                                                                1. GTZAN
                                                              2. Data Transformations
                                                                1. TorchVision Transforms
                                                                  1. Geometric Transformations
                                                                    1. Resize
                                                                      1. CenterCrop
                                                                        1. RandomCrop
                                                                          1. RandomHorizontalFlip
                                                                            1. RandomVerticalFlip
                                                                              1. RandomRotation
                                                                              2. Color Transformations
                                                                                1. ColorJitter
                                                                                  1. RandomGrayscale
                                                                                    1. Normalize
                                                                                    2. Tensor Conversions
                                                                                      1. ToTensor
                                                                                        1. ToPILImage
                                                                                      2. Transform Composition
                                                                                        1. Compose
                                                                                          1. Sequential Application
                                                                                          2. Custom Transformations
                                                                                            1. Implementing Custom Transforms
                                                                                              1. Callable Classes
                                                                                              2. Data Augmentation Strategies
                                                                                                1. Training vs Validation Transforms
                                                                                                  1. Augmentation Policies

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                                                                                              3. Building Neural Networks with torch.nn

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                                                                                              5. Training and Evaluating Models