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
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
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
Chapter 4: Building Models with `torch.nn`
The `torch.nn.Module` Base Class
Defining 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
Chapter 5: Efficient Data Handling
The Need for Specialized Data Loaders
Working with `torch.utils.data.Dataset`
Built-in Datasets (e.g., TorchVision)
Data Transformations (`torchvision.transforms`)
Using `torch.utils.data.DataLoader`
Customizing DataLoader Behavior
Hands-on Practical: Creating a Data Pipeline
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
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
Chapter 8: Monitoring and Debugging Models
Common Pitfalls 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
PyTorch Fundamentals and Setup
This chapter establishes the groundwork for using PyTorch. We begin by setting up the necessary software and environment configuration. The primary focus then shifts to PyTorch's fundamental data structure: the Tensor.
You will learn how to:
- Install PyTorch using common package managers.
- Configure a development environment suitable for PyTorch projects.
- Understand what Tensors are and their role in PyTorch.
- Create Tensors from existing data or according to specific dimensions.
- Perform essential mathematical and manipulation operations on Tensors.
- Recognize the connection between PyTorch Tensors and NumPy arrays, including conversion methods.
By the end of this chapter, you will have a functional PyTorch installation and a solid grasp of basic Tensor operations, preparing you for subsequent topics.
Sections
1.1 What is PyTorch?
1.2 Installation and Environment Configuration
1.3 Introduction to Tensors
1.4 Creating Tensors
1.5 Basic Tensor Operations
1.6 Relationship with NumPy
1.7 Hands-on Practical: Setup and Tensor Basics
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