Hands-On Deep Learning With PyTorch!

This code repository is for the book Hands-On Deep Learning with PyTorch With this book, you will learn how to use PyTorch to create deep learning models and deploy them.

Requirements

All the notebooks present in this repository are compatible with Google Colab. So there is no need to setup anything on your local machine. If you'd like to run these notebooks on your local machine create a new virtual environment and install the required packages using requirements.txt file.

$ conda create -n <my_env_name> python=3.9
$ conda activate <my_env_name>
$ python3 -m pip install requirements.txt

Chapters

The book is divided into 5 sections as follows:

Section-1 - Basic of Deep Learning and PyTorch

Section	Name	What you will Learn
Chapter-1	Foundations of Deep Learning	Learn about the basics of deep learning and how a deep learning model learns from data
Chapter-2	Building a Neural Network without any Libraries	Learn the Mathematics behind Deep learning by Building a PyTorch like Framework from Scratch
Chapter-3	Building Blocks of PyTorch	Learn about the basics of PyTorch and all of its important modules like NN, Optim, etc.
Chapter-4	Fine Tuning with PyTorch	Learn how to take a pre-trained model and fine tune it to work on your task.

Section-2 - Natural Language Processing with PyTorch

Section	Name	What you will Learn
Chapter-5	Foundations of Natural Language Processing	Learn how deep learning models understand text and Deep Learning Sequence Models like LSTM work to learn from text data.
Chapter-6	Learning Embeddings	Learn about how embeddings are learned from text data, Building Embeddings models like GLOVE and Word2Vec using PyTorch
Chapter-7	Decoding the Transformer Architecture	Learn about the Transformer Architecture and how it powers all of the state-of-the-art models from Chat-GPT to Gemini
Chapter-8	Training Large Language Models with HuggingFace	Learn how to train your own LLM from Scratch using HuggingFace. Also Learn how to fine tune existing LLM models to suit your needs.

Section-3 - Computer Vision with PyTorch

Section	Name	What you will Learn
Chapter-9	Foundations of Computer Vision & Convolution Networks	Learn about how deep learning solves different computer vision problems, Learn about the Basics of Convolution Networks which powers almost all of Computer Vision.
Chapter-10	Building Localization and Segmentation Models	Learn to build models to detect the position of an object in an image/video using PyTorch
Chapter-11	Build & Train GAN networks with PyTorch	Learn to build Generative Adversarial networks with PyTorch and use them to generate realistic images with PyTorch
Chapter-12	Exploring Different Types of GANs: CycleGAN, StyleGAN and more	Learn about how different kinds of GANs work by building them yourself in PyTorch
Chapter-13	Stable Diffusion	Learn how to create text-to-image GANs which transform your input text into a realistic image with PyTorch
Chapter-14	Vision Transformer	Learn about state-of-the-art Transformer models and how they are used in Computer Vision by building your own Vision Transformer in PyTorch

Section-4 - Recommendation Systems with PyTorch

Section	Name	What you will Learn
Chapter-15	Foundations of Recommendation Systems	Learn about how recommendation systems work and build a simple recommender in PyTorch
Chapter-16	Building Modern Recommender Systems	Learn about the tools PyTorch provides us to build recommender systems which can handle data even at massive scale.
Chapter-17	Deep Learning Recommendation Models	Learn about Deep learning models which power most recommendation systems today!
Chapter-18	Evaluating Recommendation Models	Learn how to evaluate the performance of a recommendation model in both online and offline settings.

Section-5 - Advanced PyTorch

Section	Name	What you will Learn
Chapter-19	GPU Acceleration with PyTorch	Learn how you can supercharge your training with PyTorch by using one or more GPUs!
Chapter-20	Debugging with Hooks, PDB and Tensorboard	Learn about different tools you can use to debug your code when you are building deep learning models
Chapter-21	Model Understanding with SHAP & Captum	Learn about how to interpret the inner working of your deep learning model after training them using different tools like SHAP and PyTorch's Captum
Chapter-22	Deploying PyTorch Models	Learn how to deploy your trained PyTorch models!