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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.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

Quiz for Chapter 4

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

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

Getting Started with PyTorch

Prerequisites Basic Python & ML knowledge

Level:

Intermediate

Tensor Manipulation
Create, manipulate, and perform operations on PyTorch Tensors.
Automatic Differentiation
Understand and apply PyTorch's Autograd system for gradient computation.
Neural Network Construction
Build neural networks using torch.nn modules, layers, and activation functions.
Data Loading
Prepare and load data efficiently using PyTorch Datasets and DataLoaders.
Model Training
Implement complete training and evaluation loops for deep learning models.
Model Persistence
Save and load trained PyTorch models and checkpoints.
Basic Architectures
Construct simple Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

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