CS231n: CNNs for Visual Recognition

CS231n is Stanford’s renowned course on Convolutional Neural Networks for Visual Recognition. It provides a comprehensive foundation in deep learning, from basic neural networks to state-of-the-art architectures.

Course Philosophy

The course emphasizes:

First principles understanding — implementing backpropagation from scratch
Mathematical foundations — understanding why techniques work, not just how
Practical experience — hands-on assignments with real datasets

Core Topics

Image Classification Pipeline

The course starts with the fundamental task: mapping pixels to categories.

f: \mathbb{R}^{H \times W \times C} \rightarrow \{1, 2, ..., K\}

Students implement k-NN, linear classifiers, and loss functions before moving to neural networks.

Backpropagation

The heart of deep learning—computing gradients through computational graphs:

\frac{\partial L}{\partial w} = \frac{\partial L}{\partial y} \cdot \frac{\partial y}{\partial w}

Implementing backprop from scratch builds intuition that frameworks abstract away.

CNN Architectures

Progression through landmark architectures:

LeNet → AlexNet → VGG → GoogLeNet → ResNet

Each architecture introduces key concepts: depth, skip connections, inception modules.

Interactive Overview

Explore the course structure and key concepts:

CS231n Course Overview

cs231n.stanford.edu →

🖼️Image Classification

🧠Neural Networks

📊CNNs

⚡Training

🎯Detection & Segmentation

🎨Generative Models

Core Training Loop

Forward Pass

Backward Pass

Loss Function

Gradient Descent

Prerequisites

• Linear algebra (matrices, vectors)
• Calculus (derivatives, chain rule)
• Probability basics
• Python/NumPy

Assignments

• Image classification pipeline
• Neural network from scratch
• CNN architectures
• GANs and style transfer

Why Ilya Recommended This

CS231n provides the foundational understanding of how neural networks learn visual representations— essential context for understanding modern AI systems.

Why It’s on Ilya’s List

This course provides the foundational vocabulary of modern AI:

Understanding convolution, pooling, and feature hierarchies
Intuition for optimization landscapes and training dynamics
Context for why certain architectural choices matter

Many concepts introduced in CS231n (attention, residual connections, normalization) recur throughout modern AI systems, from vision to language models.

Key Assignments

Assignment	Skills Developed
k-NN & SVM	Vectorized numpy, loss functions
Neural Networks	Backprop, modular design
CNNs	Conv layers, architectures
RNNs & Attention	Sequence modeling
GANs	Generative modeling

Resources

Course Website: https://cs231n.stanford.edu/
Video Lectures: Available on YouTube
Notes: Comprehensive written guides on cs231n.github.io

Key Insight

CS231n’s lasting value isn’t specific architectures—it’s building intuition for how neural networks learn hierarchical representations from data.