All Courses

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

Project: Building an End-to-End PyTorch Training Pipeline

Certificate

APX AI

Online

I can see the page you're looking at. Ask me anything!

All Courses

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

Project: Building an End-to-End PyTorch Training Pipeline

Certificate