Soft Computing Explained: Fuzzy Logic, Bayesian Methods, and Neural Networks
Understand soft computing through fuzzy logic, Bayesian methods, neural networks, and evolutionary search.
MachineLearning
Build GPT-2 from Scratch: Architecture, Tokenization, and Training
A first-principles guide to building GPT-2, from tokenization and embeddings to transformer training.
The Cloud Server Guide
A practical guide for setting up environments, training models, and managing GPU jobs on UOA's compute cluster
Deep Learning Explained with Mathematics: From Linear Regression to LLMs
Build deep learning intuition from first principles, progressing from linear models and backpropagation to transformers, diffusion, and LLMs.
AlphaFold Explained: Learning the Basics with PyTorch
Learn the core ideas behind AlphaFold with a PyTorch-oriented walkthrough of tensors, attention, and protein structure modeling.