Module 1 Overview: Neural Network Foundations

Time: 15-20 hours over 2 weeks

Learning Objectives

After completing this module, you will be able to:

• Mathematical Intuition: Develop a fundamental, mathematical understanding of how neural networks learn through gradient descent and backpropagation
• Implementation from Scratch: Implement a complete neural network in NumPy without high-level libraries to understand the mechanics of forward and backward propagation
• Optimization Mastery: Understand core optimization concepts including SGD, momentum, Adam, and their trade-offs
• Regularization Techniques: Apply L2 regularization and dropout to prevent overfitting and understand the bias-variance tradeoff
• Debugging Skills: Use gradient checking and systematic debugging to ensure correct implementations

Why This Module Matters

Neural networks are the foundation of modern deep learning. This module builds your understanding from the ground up, starting with simple perceptrons and progressing to multi-layer networks with backpropagation.

What makes this module essential:

• You’ll implement everything from scratch to truly understand the math
• Visual intuitions complement mathematical formalism
• Hands-on coding cements theoretical understanding
• Foundation for all subsequent modules and thesis work

Connection to Healthcare AI

This module provides critical foundations for healthcare AI work:

• Optimization strategies for complex loss landscapes in clinical data
• Regularization techniques for small medical datasets
• Custom loss function design for multimodal clinical objectives
• Debugging skills essential for healthcare AI validation
• Intuition building for understanding multimodal model behavior

Prerequisites

Before starting this module:

• Linear Algebra: Matrix multiplication, vectors, dot products
• Calculus: Derivatives, chain rule, gradients
• Python: Proficiency with NumPy for numerical computations
• Basic ML: Understanding of supervised learning concepts

Recommended preparation:

• Khan Academy’s linear algebra course
• 3Blue1Brown’s essence of calculus series

Module Path

Follow Neural Network Foundations Learning Path for the complete week-by-week curriculum.

Key concepts covered:

1. Linear Classifiers - SVM and softmax foundations
2. Perceptron - Single neuron architecture
3. Multi-Layer Perceptrons - Deep networks
4. Backpropagation - The learning algorithm
5. Optimization - SGD to Adam
6. Regularization - Preventing overfitting
7. Dropout - Stochastic regularization
8. Bias-Variance Tradeoff - Generalization theory
9. Training Practices - Initialization, debugging, hyperparameters
10. MNIST from Scratch - Complete implementation

Critical Checkpoints

Must complete before proceeding to Module 2:

• ✅ Watched CS231n Lectures 1-4
• ✅ Watched all 7 parts of “Neural Networks Demystified”
• ✅ Completed CS231n Assignment 1 (all sections)
• ✅ Can implement backpropagation from scratch without references
• ✅ Understand gradient checking and why it’s necessary
• ✅ Can explain the difference between SGD, momentum, and Adam
• ✅ Understand L2 regularization and dropout

Time Breakdown

Total: 15-20 hours over 2 weeks

• Videos: 3-4 hours (CS231n lectures 1-4, Welch Labs series)
• Reading: 3-4 hours (concept pages, papers)
• CS231n Assignment 1: 8-15 hours (implement everything from scratch)
• Additional exercises: 2-3 hours

Key Takeaway

There’s no substitute for coding everything from scratch at least once.

Libraries hide crucial details. The theoretical knowledge from lectures becomes real when you debug your own backpropagation code. Deep understanding comes from implementation—embrace the struggle, that’s where learning happens.

Next Steps

After completing this module:

1. Module 2: Computer Vision with CNNs
2. Module 3: Attention and Transformers
3. Module 4: Language Models with NanoGPT

Ready to start? Begin with Neural Network Foundations Learning Path.