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
Advanced Tensor Manipulations
Building upon the foundational tensor operations covered previously, this chapter introduces techniques for more advanced tensor manipulation. Gaining fine-grained control over tensor structure, data types, and device placement is fundamental for preparing data and implementing complex deep learning models effectively.
In this chapter, you will learn to:
- Select and modify tensor elements precisely using indexing and slicing methods.
- Restructure tensors without changing their data using
view(),reshape(), andpermute(). - Combine multiple tensors or divide a single tensor into parts with
cat(),stack(),split(), andchunk(). - Apply operations between tensors of compatible but different shapes using PyTorch's broadcasting mechanism.
- Work with different numerical data types (e.g.,
float,int) and cast tensors accordingly. - Transfer tensors between CPU memory and GPU accelerators using
.to(device).
Mastering these manipulations is necessary for handling the diverse data formats and computational requirements encountered in deep learning workflows.
Sections
2.1 Tensor Indexing and Slicing
2.2 Reshaping and Rearranging Tensors
2.3 Joining and Splitting Tensors
2.4 Understanding Broadcasting
2.5 Tensor Data Types
2.6 CPU vs GPU Tensors
2.7 Practice: Tensor Manipulation Techniques
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