Deep Learning Foundations
Machine learning using multi-layer neural networks.
CurrentintermediateFull course available
Overview
Deep learning uses neural networks with many layers to learn increasingly abstract representations of data, and is the technique underlying modern LLMs, image recognition, and speech systems. This entry covers the intuition -- neurons, layers, weights -- not the calculus of backpropagation.
- What it is
- A family of machine learning techniques using multi-layer neural networks.
- Why it's used
- Deep networks can learn complex patterns (language, images) that simpler ML models struggle to capture directly from raw data.
- Where it fits
- A subset of machine learning; the technique underlying large language models and most modern generative AI.
Core concepts
- Neurons and layers
- Weights and activation functions
- Forward pass (intuition, not the training math)
- Why 'deep' means many layers
Example
Each layer builds on the previous layer's output -- the 'depth' in deep learning refers to this stack of increasingly abstract transformations.
// Intuition, not real code: each layer transforms its input
// into a slightly more abstract representation.
// Layer 1: raw pixels -> edges
// Layer 2: edges -> shapes
// Layer 3: shapes -> "this is a cat"Common use cases
- Image and speech recognition
- Large language models
- Any task with large amounts of complex, unstructured training data
Project ideas
- Sketch (on paper) what the layers of a simple image classifier might each be learning, from raw pixels up to a final label