Explores SVMs, feature maps, and the importance of finding the maximum margin solution for classification problems.
Covers a crash course on deep learning, including the Mark I Perceptron, neural networks, optimization algorithms, and practical training aspects.
Introduces the fundamentals of deep learning, covering neural networks, CNNs, special layers, weight initialization, data preprocessing, and regularization.
Explains how PCA eliminates dimensions by finding principal components with most variation and compares PCA with Kernel PCA.
