Field
Deep Learning
Networks with many layers, and the machinery that makes them learn.
Deep learning is the reason AI stopped being a research curiosity and started working. The core idea is old — stack simple units into layers, adjust their weights until the output is right — and the algorithm that adjusts them, backpropagation, dates to 1986.
What changed was scale. Large labelled datasets arrived, GPUs made the arithmetic cheap, and a thirty-year-old idea suddenly beat everything else. That's the pattern worth internalising: the bottleneck was rarely ideas.
This field covers the machinery. How networks learn (backpropagation, gradient descent, loss functions), the architectures that dominate (transformers, CNNs), the mechanism underneath modern AI (attention), and the shortcut nearly everyone takes (transfer learning).
Start with Neural Network if you want the ground floor, or Transformer if you want to understand what's actually running inside the tools you use.
9 concepts in this field
Transformer
The neural-network architecture behind almost every modern AI model — built around attention, which lets it weigh every word against every other, all at once.
Embeddings
Turning words (or images, or anything) into lists of numbers, arranged so that similar meanings end up close together.
Attention
The mechanism that lets an AI decide which other words matter when interpreting each word — the core idea behind transformers.
Neural Network
A system of simple connected units that learns patterns from examples — the foundation underneath deep learning and modern AI.
Backpropagation
The algorithm that works out which weights caused a mistake and by how much — the reason neural networks can learn at all.
Gradient Descent
Walking downhill on the error surface, one small step at a time — how a model's weights actually get updated.
Loss Function
The number that says how wrong the model is — and therefore the definition of what it's trying to become.
CNN (Convolutional Neural Network)
A network that slides small filters across an image to find local patterns — the architecture that made computer vision work.
Transfer Learning
Starting from a model that already learned something general, instead of from random numbers — why small teams can build real AI.