Covers model-free prediction methods in reinforcement learning, focusing on Monte Carlo and Temporal Differences for estimating value functions without transition dynamics knowledge.
Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.
Explores bug-finding, verification, and the use of learning-aided approaches in program reasoning, showcasing examples like the Heartbleed bug and differential Bayesian reasoning.
Delves into deep learning's dimensionality, data representation, and performance in classifying large-dimensional data, exploring the curse of dimensionality and the neural tangent kernel.
