Building Blocks of Deep Learning

Deep learning can feel intimidating until you see the big picture laid out clearly. This quick guide walks through the key concepts from the ground up.

  1. Perceptrons — the atomic unit: weighted sum + activation function
  2. Activation Functions — ReLU, Sigmoid, Tanh, GELU — introducing non-linearity
  3. Backpropagation — gradient descent through the chain rule
  4. Convolutional Layers (CNNs) — spatial feature extraction for images
  5. Recurrent Networks (RNNs/LSTMs) — sequential data with memory
  6. Attention Mechanisms — relating every token to every other token
  7. Transformers — the architecture underpinning GPT and its successors

"Attention is all you need." — Vaswani et al., 2017

Each concept is paired with an intuitive diagram and a one-sentence explanation so you can build a solid mental model before diving into full tutorials or research papers.