Introduces Convolutional Neural Networks (CNNs) for autonomous vehicles, covering architecture, applications, and regularization techniques.
Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.
Explores deep learning fundamentals, including image classification, neural network working principles, and machine learning challenges.
Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
