Foundation Modules Overview

Foundation

The foundation modules (Weeks 1-5) provide essential deep learning knowledge. These modules build your understanding from the ground up, starting with neural network fundamentals and progressing through computer vision, attention mechanisms, and language models.

Complete Curriculum Path

Follow Deep Learning Foundations Path for the complete 5-week curriculum covering all four modules.

Total time: 54-73 hours over 5 weeks

Module 1: Neural Network Foundations

Duration: 2 weeks | Hours: 15-20 hours

Build deep understanding of neural networks, from forward propagation to backpropagation and optimization algorithms. Implement everything from scratch to truly understand how neural networks learn.

Learn: Module 1 Overview → NN Foundations Path

Module 2: Computer Vision with CNNs

Duration: 1-2 weeks | Hours: 12-18 hours

Master convolutional neural networks for image understanding. Learn the architectural evolution from AlexNet to ResNet and apply transfer learning to new tasks.

Learn: Module 2 Overview → CNN Foundations Path

Module 3: Attention and Transformers

Duration: 1-2 weeks | Hours: 12-16 hours

Understand the most important architectural innovation in modern AI. Master self-attention and the transformer architecture that revolutionized NLP and now dominates AI.

Learn: Module 3 Overview → Transformers Path

Module 4: Language Models with NanoGPT

Duration: 2 weeks | Hours: 15-25 hours

Implement GPT from scratch with Andrej Karpathy. Gain complete, code-level understanding of decoder-only transformers and autoregressive language models.

Learn: Module 4 Overview → NanoGPT Path

Prerequisites

Before starting:

Linear Algebra: Matrix operations, vectors, dot products
Calculus: Derivatives, chain rule, gradients
Python: Proficiency with NumPy and PyTorch basics
Basic ML: Understanding of supervised learning concepts

Why Foundation Modules Matter

These modules provide the essential knowledge for all modern AI:

Neural Networks: The building blocks of deep learning
CNNs: Visual understanding for computer vision
Transformers: The architecture powering GPT, BERT, CLIP, and modern AI
Language Models: Understanding how LLMs work from the inside

For Healthcare AI:

Neural networks for clinical prediction models
CNNs for medical imaging (X-rays, CT, MRI, pathology)
Transformers for EHR event sequences and clinical text
Language models for patient trajectory prediction

Learning Philosophy

Implementation is understanding.

These modules emphasize hands-on coding over passive learning. You’ll implement neural networks from scratch (NumPy), build CNNs (PyTorch), code attention mechanisms, and implement GPT line-by-line. The struggle of debugging your own code is where deep understanding emerges.

Time Investment

Total: 54-73 hours over 5 weeks

Module 1: 15-20 hours (neural networks from scratch)
Module 2: 12-18 hours (CNNs and architectures)
Module 3: 12-16 hours (attention and transformers)
Module 4: 15-25 hours (build GPT from scratch)

Recommendation: Don’t rush. Deep understanding takes time. Better to spend an extra week and truly understand backpropagation than to move forward with shaky foundations.

Critical Checkpoints

After completing foundations, you should be able to:

✅ Implement backpropagation from scratch without references
✅ Understand gradient descent, SGD, momentum, and Adam
✅ Explain convolution as spatial filtering and calculate receptive fields
✅ Understand skip connections and why they enable deep networks
✅ Implement scaled dot-product attention from scratch
✅ Explain the complete transformer architecture (encoder and decoder)
✅ Understand causal masking for autoregressive generation
✅ Implement a GPT model and generate text

Next Steps

After completing foundations:

Advanced Modules: Advanced Topics Overview
Multimodal AI: Vision-Language Models
Generative AI: Diffusion Models
Healthcare AI: Healthcare Specialization
Research: Research Methodology

Ready to start? Begin with Deep Learning Foundations Path or jump directly to Module 1.