Category: machine learning

Practical explanations of machine learning concepts, model architectures, and learning methods—from language representations and transformers to reinforcement learning and scientific applications.
Start here:
Word Embeddings Explained: The Math Behind AI, LLMs, and Chatbots — understand how language is represented as vectors and why embeddings matter.
Physics-Informed Machine Learning — explore how physical knowledge can be incorporated into machine-learning models.
Introduction to Deep Reinforcement Learning — review the core idea of learning through actions, rewards, and feedback.
The Annotated Transformer — follow the architecture behind modern language models in annotated code.