A practical guide to ML and DL foundations, regularization, hyperparameter tuning, CNNs, RNNs, Transformers, evaluation, and the model lifecycle.
A practical walkthrough of value-based and policy-based reinforcement learning, MDPs, and the main algorithms for engineers getting started with RL.