PyTorch Tutorials

PyTorch Tutorials Roadmap

Section 1: Introduction to PyTorch and Deep Learning Basics

  • What is PyTorch?
    • An open-source machine learning framework.
    • Known for its flexibility, ease of use, and dynamic computation graph.
    • Popular for research and production.
  • Why Learn PyTorch?
    • Widely used in cutting-edge AI research.
    • Great for building and training neural networks.
    • Strong community support.
    • Integration with Python ecosystem.
  • Deep Learning Fundamentals Review:
    • What is deep learning?
    • Neural networks: layers, neurons, activation functions.
    • Forward and backward propagation.
    • Loss functions.
    • Optimizers.
  • Setting up Your Environment:
    • Installing PyTorch (with or without CUDA support).
    • Using virtual environments.
    • Choosing an IDE or text editor.
    • Ensuring GPU acceleration is set up (if available).

Section 2: PyTorch Basics: Tensors

  • What are Tensors?
    • Multi-dimensional arrays (similar to NumPy arrays).
    • The fundamental data structure in PyTorch.
  • Creating Tensors:
    • From Python lists and NumPy arrays.
    • Using built-in functions (torch.zeros, torch.ones, torch.rand, torch.arange).
  • Tensor Attributes:
    • shape, dtype, device.
  • Tensor Operations:
    • Arithmetic operations (+, -, *, /).
    • Matrix multiplication (torch.matmul, @ operator).
    • Element-wise operations.
    • Broadcasting.
  • Indexing and Slicing Tensors:
    • Accessing elements and sub-tensors.
    • Advanced indexing (fancy indexing, boolean indexing).
  • Reshaping and Manipulating Tensors:
    • reshape(), view(), squeeze(), unsqueeze().
    • Concatenation (torch.cat).
    • Stacking (torch.stack).
  • Moving Tensors to Different Devices (CPU/GPU):
    • Using .to().
    • Checking for CUDA availability.
  • NumPy Conversion:
    • Converting between PyTorch tensors and NumPy arrays (.numpy(), torch.from_numpy()).

Section 3: Autograd: Automatic Differentiation

  • Understanding Automatic Differentiation:
    • How PyTorch computes gradients automatically.
    • The backbone of training neural networks.
  • The requires_grad Attribute:
    • Tracking operations for gradient computation.
  • The Computation Graph:
    • Understanding how PyTorch builds the graph of operations.
  • Computing Gradients:
    • Using .backward().
    • Accessing gradients with .grad.
  • Disabling Gradient Calculation:
    • Using torch.no_grad() context manager.
    • Using .detach().
    • Why disable gradients (inference, evaluation).

Section 4: Building Neural Networks with torch.nn

  • The torch.nn Module:
    • Building blocks for neural networks.
  • Defining Neural Network Architectures:
    • Subclassing nn.Module.
    • The __init__() method (defining layers).
    • The forward() method (defining the forward pass).
  • Common Layers:
    • Linear (nn.Linear).
    • Convolutional (nn.Conv2d).
    • Pooling (nn.MaxPool2d, nn.AvgPool2d).
    • Activation functions (nn.ReLU, nn.Sigmoid, nn.Tanh).
    • Dropout (nn.Dropout).
    • Batch Normalization (nn.BatchNorm2d).
  • Sequential Models:
    • Using nn.Sequential for simple layer stacks.
  • Accessing and Modifying Model Parameters:
    • model.parameters().
    • model.state_dict().
  • Moving Models to Different Devices:
    • Using .to().

Section 5: Training Neural Networks

  • Loss Functions:
    • Understanding the role of loss functions.
    • Common loss functions (nn.CrossEntropyLoss, nn.MSELoss, nn.BCELoss).
  • Optimizers:
    • Understanding the role of optimizers.
    • Common optimizers (torch.optim.SGD, torch.optim.Adam, torch.optim.RMSprop).
    • Learning rate.
  • The Training Loop:
    • Forward pass.
    • Calculating the loss.
    • Backward pass (computing gradients).
    • Optimizer step (updating weights).
    • Zeroing gradients (optimizer.zero_grad()).
  • Training with Batches:
    • Understanding batching and its benefits.

Section 6: Data Loading and Preprocessing with torch.utils.data

  • Understanding Datasets and DataLoaders:
    • The Dataset class (representing your data).
    • The DataLoader class (iterating over datasets in batches).
  • Creating Custom Datasets:
    • Subclassing Dataset.
    • Implementing __len__() and __getitem__().
  • Using Built-in Datasets (e.g., MNIST, CIFAR-10):
    • Using torchvision.datasets.
  • Data Transformations:
    • Using torchvision.transforms for image data.
    • Composing transformations.

Section 7: Saving and Loading Models

  • Saving Model State:
    • Saving the model's state dictionary (model.state_dict()).
    • Saving the optimizer's state dictionary.
  • Loading Model State:
    • Loading the state dictionary into a model.
    • Loading the optimizer's state dictionary.
  • Saving and Loading Entire Models (Cautionary Note):
    • Saving the entire model object (less portable).

Section 8: Model Evaluation and Inference

  • Putting the Model in Evaluation Mode:
    • Using model.eval().
    • Disabling gradient calculation with torch.no_grad().
  • Calculating Metrics:
    • Accuracy, precision, recall, F1-score (depending on the task).
  • Making Predictions (Inference):
    • Passing data through the trained model.

Section 9: Building More Complex Models and Concepts

  • Convolutional Neural Networks (CNNs):
    • Architecture of CNNs (convolutional layers, pooling layers).
    • Building and training a simple CNN for image classification.
  • Recurrent Neural Networks (RNNs):
    • Understanding sequences.
    • Simple RNNs, LSTMs (Long Short-Term Memory), GRUs (Gated Recurrent Units).
    • Building and training a simple RNN for sequence data.
  • Transfer Learning:
    • Using pre-trained models.
    • Fine-tuning pre-trained models.
  • Regularization Techniques:
    • Dropout, L1/L2 regularization.
  • Learning Rate Scheduling:
    • Adjusting the learning rate during training.
  • Using Multiple GPUs (Introduction):
    • nn.DataParallel.

Section 10: Advanced PyTorch Features and Ecosystem

  • TorchScript:
    • Tracing and scripting models for deployment.
  • PyTorch Lightning:
    • A lightweight wrapper for organizing PyTorch code.
    • Simplifying the training loop and boilerplate.
  • TorchServe:
    • Serving PyTorch models for production.
  • TorchVision, TorchText, TorchAudio:
    • Domain-specific libraries for computer vision, natural language processing, and audio.
  • Model Interpretability (Introduction):
    • Techniques for understanding model decisions.

Section 11: Projects and Specialization

  • Work on a Small Project:
    • Image classification on a custom dataset.
    • Text classification.
    • Sequence generation.
  • Explore Specific Areas:
    • Computer Vision (object detection, segmentation).
    • Natural Language Processing (transformers, sentiment analysis).
    • Reinforcement Learning.
    • Generative Adversarial Networks (GANs).
  • Read Research Papers and Implement Them in PyTorch.

Section 12: Further Learning and Community

  • Official PyTorch Documentation (pytorch.org/docs).
  • PyTorch Tutorials (pytorch.org/tutorials).
  • Online Courses and Specializations (Coursera, edX, Udacity, fast.ai).
  • Books on Deep Learning with PyTorch.
  • Participating in Community Forums (PyTorch Forums, Stack Overflow).
  • Attending Conferences and Meetups.
  • Exploring Open-Source PyTorch Projects on GitHub.