Getting Started with PyTorch
Chapter 1: PyTorch Fundamentals and Setup
What is PyTorch?
Installation and Environment Configuration
Introduction to Tensors
Creating Tensors
Basic Tensor Operations
Relationship with NumPy
Hands-on Practical: Setup and Tensor Basics
Quiz for Chapter 1
Chapter 2: Advanced Tensor Manipulations
Tensor Indexing and Slicing
Reshaping and Rearranging Tensors
Joining and Splitting Tensors
Understanding Broadcasting
Tensor Data Types
CPU vs GPU Tensors
Practice: Tensor Manipulation Techniques
Quiz for Chapter 2
Chapter 3: Automatic Differentiation with Autograd
The Concept of Automatic Differentiation
PyTorch Computation Graphs
Tensors and Gradient Calculation (
requires_grad)Performing Backpropagation (
backward())Accessing Gradients (
.grad)Disabling Gradient Tracking
Gradient Accumulation
Hands-on Practical: Autograd Exploration
Quiz for Chapter 3
Chapter 4: Building Models with
torch.nnThe
torch.nn.Module Base ClassDefining Custom Network Architectures
Common Layers: Linear, Convolutional, Recurrent
Activation Functions (ReLU, Sigmoid, Tanh)
Sequential Containers for Simple Models
Loss Functions (
torch.nn losses)Optimizers (
torch.optim)Practice: Building a Simple Network
Quiz for Chapter 4
Chapter 5: Efficient Data Handling
The Need for Specialized Data Loaders
Working with
torch.utils.data.DatasetBuilt-in Datasets (e.g., TorchVision)
Data Transformations (
torchvision.transforms)Using
torch.utils.data.DataLoaderCustomizing DataLoader Behavior
Hands-on Practical: Creating a Data Pipeline
Quiz for Chapter 5
Chapter 6: Implementing the Training Loop
Anatomy of a Training Loop
Setting Up the Model, Loss, and Optimizer
Iterating Through Data with DataLoader
The Forward Pass: Getting Predictions
Calculating the Loss
Backpropagation: Computing Gradients
Updating Weights with the Optimizer
Zeroing Gradients
Implementing an Evaluation Loop
Saving and Loading Model Checkpoints
Hands-on Practical: Complete Training Routine
Quiz for Chapter 6
Chapter 7: Introduction to Common Architectures
Convolutional Neural Networks (CNNs) Overview
Building a Simple CNN in PyTorch
Understanding Input/Output Shapes for CNN Layers
Recurrent Neural Networks (RNNs) Overview
Building a Simple RNN in PyTorch
Handling Sequential Data Input for RNNs
Brief Mention of LSTM and GRU
Practice: Implementing Basic CNN and RNN
Quiz for Chapter 7
Chapter 8: Monitoring and Debugging Models
Common Errors in PyTorch Development
Debugging Shape Mismatches
Checking Device Placement (CPU/GPU)
Inspecting Gradients for Issues (Vanishing/Exploding)
Visualizing Training Progress with TensorBoard
Logging Metrics during Training/Evaluation
Using Python Debugger (pdb) with PyTorch
Practice: Debugging and Visualization
Quiz for Chapter 8
Backpropagation: Computing Gradients
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- Automatic differentiation package - torch.autograd, PyTorch team, 2024 - Official documentation covering PyTorch's automatic differentiation engine (Autograd), computation graphs, and the mechanics of
loss.backward(). - Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A comprehensive textbook that details the theoretical foundations of backpropagation and automatic differentiation, including the chain rule.
- Learning representations by back-propagating errors, David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams, 1986 Nature, Vol. 323 (Springer Nature) DOI: 10.1038/323533a0 - The foundational academic paper that introduced the backpropagation algorithm for training multi-layer neural networks.
- CS231n: Deep Learning for Computer Vision. Backpropagation, Intuitions, Stanford University CS231n course staff, 2024 - Lecture notes providing an intuitive and visual explanation of the backpropagation algorithm and its relation to computation graphs.