Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.
Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.
Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Explores reinforcement learning in AI to master games using neural networks.
