Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Explores Portfolio Theory with a focus on the Risk Parity Strategy, discussing asset allocation proportional to the inverse of volatility and comparing different diversified portfolios.
Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
