The foundations of Machine Learning and Deep Learning
A practical guide to ML and DL foundations, regularization, hyperparameter tuning, CNNs, RNNs, Transformers, evaluation, and the model lifecycle.
Machine Learning
Technical notes
Writing
Practical engineering, written up properly. Not academic papers — these are things I built or worked out (recommendation, forecasting, conversational AI, and how machines learn to decide — up to reinforcement learning), documented with the structure and rigor of research notes so they stay reliable and reusable. Build, teach, iterate.
Reinforcement Learning: All the Basics
A practical walkthrough of value-based and policy-based reinforcement learning, MDPs, and the main algorithms for engineers getting started with RL.
Reinforcement Learning