Deep Learning

Neural networks and what they buy you: real power on unstructured data, and a poor trade on most tables.

10 articles, newest first.

• NLP · May 14, 2026 · 3 min Embeddings, explained for people who ship things What embedding vectors actually are, what they are good for besides chatbots, and the operational details — normalisation, chunking, drift — that tutorials skip.
• Generative models and LLMs · Part 26 of 33 · 7 min Attention is the whole trick Queries, keys and values in plain language: how a word picks up meaning from its neighbours, and why one matrix multiplication solved the long-range dependency problem.
• Generative models and LLMs · Part 25 of 33 · 7 min Transformers explained from scratch Tokens, embeddings and a stack of identical blocks — how a transformer turns next-word prediction into a training objective, and why it trains in parallel where RNNs could not.
• Neural networks · Part 23 of 33 · 7 min Convolution, and how computers learned to see What a convolution actually does to an image, why sliding a tiny grid of numbers finds edges, and why learning those numbers instead of designing them changed computer vision.
• Generative models and LLMs · Part 31 of 33 · 8 min How diffusion models make images The idea behind AI image generators, explained plainly: teach a model to remove noise from a picture, and running it in reverse from pure static gives you a picture.
• Neural networks · Part 24 of 33 · 7 min What word embeddings really are Why representing words as IDs fails, how counting the company a word keeps turns language into geometry, and what the famous king-minus-man analogy does and does not prove.
• Generative models and LLMs · Part 32 of 33 · 7 min GANs, and why deepfakes work How training two networks against each other produces convincing fake images, why that setup is famously unstable, and what it sensibly changes about trusting a video.
• Neural networks · Part 22 of 33 · 7 min Backpropagation explained plainly How a network works out which weight was to blame for a wrong answer, why the chain rule is really credit assignment, and why backprop is bookkeeping rather than magic.
• Neural networks · Part 21 of 33 · 6 min Gradient descent from first principles The cost surface, why the gradient points uphill, what the learning rate really controls, and why local minima matter far less in practice than everyone expects.
• Neural networks · Part 20 of 33 · 7 min What a neural network actually is Layers, weights, biases and activations built up from scratch on the handwritten-digit problem, and why "learning" means nothing more exotic than adjusting a pile of numbers.