Build a Deep Learning Library in Python

Build a Simple Deep Learning Library in Python (From Scratch)

Learn how deep learning frameworks work by building your own minimal PyTorch-style library from the ground up. Built with Python and NumPy.

This book is designed for developers and ML learners who want to go beyond using frameworks and truly understand what happens under the hood

What You’ll Build

• Autograd Engine – automatic differentiation from scratch
• Neural Network Modules – layers, activations, and loss functions
• Optimizers – SGD, Adam
• Model Persistence – save and load trained models
• Training Loop – a clean, reusable trainer
• Datasets & Dataloaders – batching, shuffling, iteration
• Parameter Initialization – common initialization strategies
• Convolutional Neural Networks (CNNs) – build and train conv nets

What You’ll Train (Using the Library)

• MNIST – fully train a neural network from scratch
• Simple CNN on MNIST
• CNN on CIFAR-10
• Simple ResNet on CIFAR-10