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Covers the fundamentals of scaling to massive data using Spark, focusing on RDDs, transformations, actions, Spark architecture, and Spark's machine learning toolkit.
Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.
Explores the history, models, training, convergence, and limitations of neural networks, including the backpropagation algorithm and universal approximation.