Phase 2: Advanced Training Techniques · ~120 minutes · Python

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.

What you will learn

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)

Introduction

Type: Build
Languages: Python
Prerequisites: Earlier lessons in this course
Time: ~120 minutes

Objective

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)

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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