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.

Hiring signal: Understanding of build your own mini framework internals

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: All of Phase 03 (Lessons 01-09) Time: ~120 minutes

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 It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

Create a free account to unlock Phase 0 and Phase 1 of every course — no credit card.