Reinforcement Learning: Q-Learning

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Explores Deep Learning Agents in Reinforcement Learning, emphasizing neural network approximations and challenges in training multiagent systems.

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

Explores the impact of Deep learning on Digital Humanities, focusing on non conceptual knowledge systems and recent advancements in AI.

Explores reinforcement learning in AI to master games using neural networks.

Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

Delves into deep learning optimization, challenges, SGD variants, critical points, overparametrized networks, and adaptive methods.