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Deep Learning Foundations

The foundation modules (Weeks 1-5) establish the core concepts you need for advanced deep learning research. These modules progressively build your understanding from basic neural networks to modern transformer architectures.

Overview

This learning path covers four sequential modules that form the foundation of modern deep learning:

  1. Neural Network Foundations - Mathematical understanding of how networks learn
  2. Convolutional Neural Networks - Computer vision and spatial processing
  3. Attention and Transformers - Sequence modeling and the transformer revolution
  4. Language Models (GPT) - Autoregressive generation and large language models

Learning Objectives

By completing this path, you will:

  • Mathematical Intuition: Understand gradient descent, backpropagation, and optimization
  • Architecture Mastery: Know CNNs, transformers, and GPT architectures in depth
  • Implementation Skills: Build neural networks, CNNs, and transformers from scratch
  • Research Foundation: Have the conceptual base for advanced AI research

Prerequisites

Before starting this path, ensure you have:

  • ✓ Linear algebra fundamentals (vectors, matrices, matrix multiplication)
  • ✓ Calculus (derivatives, chain rule, gradients)
  • ✓ Python programming proficiency
  • ✓ Basic understanding of supervised learning

Recommended preparation:

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

Module 1: Neural Network Foundations

Duration: 1-2 weeks | Hours: 15-20 hours

Build a deep understanding of how neural networks work, from forward propagation to backpropagation and optimization algorithms.

Core Concepts

  1. Linear Classifiers - SVM and softmax foundations
  2. Perceptron - Single neuron architecture
  3. Multi-Layer Perceptrons - Deep networks with hidden layers
  4. Backpropagation - The core learning algorithm
  5. Optimization Algorithms - SGD, momentum, Adam
  6. Regularization - L2 weight decay and early stopping
  7. Dropout - Preventing co-adaptation
  8. Bias-Variance Tradeoff - Understanding generalization
  9. Training Practices - Weight init, learning rates, debugging

Learning Resources

  • Videos:
    • Welch Labs: Neural Networks Demystified (7 parts)
    • 3Blue1Brown: Neural Networks series (4 videos)
    • CS231n Lectures 1-4 (Stanford)
  • Reading:
    • CS231n Notes: Optimization and Backpropagation
    • Neural Networks and Deep Learning (Michael Nielsen, Chapters 1-2)
  • Hands-on:
    • CS231n Assignment 1 (KNN, SVM, Softmax, Two-Layer Net)

Hands-On Project

MNIST Classification from Scratch - Build a neural network in NumPy without high-level libraries

Critical Checkpoints

  • 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
  • Completed CS231n Assignment 1

Next Module

Once you’ve completed these checkpoints, proceed to Module 2: CNNs.

Module 2: Convolutional Neural Networks

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

Learn the fundamentals of computer vision and convolutional neural networks, understanding how CNNs process and understand images.

Core Concepts

  1. Convolution Operations - Kernels, filters, and feature maps
  2. Pooling Layers - Spatial downsampling (max, average, global)
  3. Transfer Learning - Pre-training and fine-tuning strategies

Architecture Papers

  1. AlexNet (2012) - The breakthrough that started the deep learning revolution
  2. VGG (2014) - Demonstrated importance of depth with simple 3×3 design
  3. ResNet (2015) - Revolutionary skip connections enabling 100+ layer networks

Learning Resources

  • Videos:
    • CS231n Lectures 5-9: CNNs for Visual Recognition
  • Papers:
    • AlexNet, VGG, ResNet (read all three)
  • Hands-on:
    • CS231n Assignment 2 (CNN implementation and training)

Critical Checkpoints

  • Understand convolution operation and receptive fields
  • Can explain why skip connections enable very deep networks
  • Understand batch normalization
  • Know when to use transfer learning vs training from scratch
  • Can implement a CNN in PyTorch

Next Module

With vision understanding established, move to Module 3: Attention and Transformers.

Module 3: Attention and Transformers

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

Master the attention mechanism and transformer architecture that revolutionized NLP and now dominates many areas of AI.

Core Concepts

  1. RNN Limitations - Why recurrent architectures struggle
  2. Attention Mechanism - Query-key-value framework
  3. Scaled Dot-Product Attention - The core transformer operation
  4. Multi-Head Attention - Parallel attention mechanisms

Architecture Papers

  1. Attention Is All You Need (2017) - The transformer architecture

Learning Resources

  • Videos:
    • CS231n: Attention and Transformers lecture
    • 3Blue1Brown: Attention in transformers
  • Reading:
    • The Illustrated Transformer (Jay Alammar)
    • The Annotated Transformer (Harvard NLP)
  • Papers:
    • “Attention Is All You Need” (Vaswani et al., 2017) - Read 3 times minimum

The Most Important Equation in Modern AI

Attention(Q,K,V)=softmax(QKTdk)V\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V

Critical Checkpoints

  • Can implement scaled dot-product attention from scratch
  • Can implement multi-head attention from scratch
  • Can draw and explain the complete transformer architecture
  • Understand positional encodings and why they’re needed
  • Understand encoder-decoder attention vs self-attention

Next Module

With transformers mastered, implement a complete language model in Module 4.

Module 4: Language Models with NanoGPT

Duration: 1-2 weeks | Hours: 15-25 hours

Implement a GPT-style language model from scratch, understanding autoregressive generation and modern LLM architectures.

Core Concepts

  1. Tokenization - BPE and subword tokenization
  2. Causal Attention - Masked self-attention for autoregressive generation
  3. GPT Architecture - Decoder-only transformer design
  4. Language Model Training - Training techniques for LMs
  5. Text Generation - Sampling strategies (greedy, top-k, top-p, beam search)

Learning Resources

  • Videos:
    • Andrej Karpathy: “Let’s Build GPT” (2-hour video) - Code along, don’t just watch
  • Code:
    • NanoGPT repository walkthrough
  • Papers:
    • “Language Models are Unsupervised Multitask Learners” (GPT-2 paper)

Hands-On Project

Implement NanoGPT from scratch following Karpathy’s tutorial. Train on a small dataset (Shakespeare text).

Critical Checkpoints

  • Understand BPE tokenization
  • Can implement causal attention masking
  • Built complete GPT model from scratch
  • Understand gradient accumulation and why it’s needed
  • Can generate text with different sampling strategies
  • Understand the difference between encoder-only, decoder-only, and encoder-decoder transformers

Path Completion

You have completed the Deep Learning Foundations path when you:

  • ✅ Can implement backpropagation from scratch
  • ✅ Can build CNNs in PyTorch
  • ✅ Can implement transformers from scratch
  • ✅ Built a working GPT model
  • ✅ Understand all core deep learning concepts at a mathematical level
  • ✅ Can read and understand research papers in deep learning

Success Tips

  • Implement from scratch: Don’t just use libraries; understand the math
  • Visualize: Draw diagrams of architectures and data flow
  • Experiment: Modify hyperparameters and observe the effects
  • Read papers: Don’t skip the foundational papers (AlexNet, ResNet, Attention Is All You Need)
  • Code along: Especially for the NanoGPT tutorial - passive watching won’t work

Next Steps

After completing this foundational path, you can:

  1. Advanced Topics: Explore multimodal models, diffusion models, and self-supervised learning
  2. Domain Applications: Apply these foundations to healthcare AI, scientific computing, or other domains
  3. Research: Begin working on novel architectures or training techniques

Time Investment

Total estimated time: 45-60 hours over 5 weeks

  • Module 1: 15-20 hours
  • Module 2: 12-18 hours
  • Module 3: 12-16 hours
  • Module 4: 15-25 hours

Recommendation: Don’t rush. Deep understanding takes time. It’s better to spend an extra week truly mastering the foundations than to move forward with gaps in understanding.

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.”

OverviewNeural Networks