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
Performing Backpropagation (backward())
Was this section helpful?
Jul 19, 2025
Fixed an error in the backward() method section. Replaced an incorrect code example with an accurate one to correctly demonstrate the runtime error for non-scalar tensors and its proper resolution.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - Provides a theoretical explanation of backpropagation, computational graphs, and the chain rule, which are the mathematical basis for
torch.autograd.backward(). - CS231n: Deep Learning for Computer Vision - Lecture Notes on Backpropagation, Intuitions, Andrej Karpathy, Justin Johnson, and Fei-Fei Li, 2017 - Offers an understanding of how backpropagation works in the context of neural networks, complementing PyTorch's implementation details.
- Automatic Differentiation with
torch.autograd(PyTorch Tutorial), PyTorch Developers, 2024 (PyTorch) - A code-focused guide to usingautograd, demonstrating thebackward()method in various scenarios and explaining its interaction with the computation graph.