AI Engineering/Phase 03/Lesson 10

Build Your Own Mini Framework

You have built neurons, layers, networks, backprop, activations, loss functions, optimizers, regularization, initialization, and LR schedules. All as separate pieces. Now wire them together into a framework. Not PyTorch. Not TensorFlow. Yours.

Build 2 hr Python

Prerequisites: All of Phase 03 (Lessons 01-09)

#Learning Objectives

Build a complete deep learning framework (~500 lines) with Module, Linear, ReLU, Sigmoid, Dropout, BatchNorm, Sequential, loss functions, optimizers, and DataLoader
Explain the Module abstraction (forward, backward, parameters) and why train/eval mode toggling is necessary
Wire all components into a working training loop that trains a 4-layer network on circle classification
Map each component of your framework to its PyTorch equivalent (nn.Module, nn.Sequential, optim.Adam, DataLoader)

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.