Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Explores the evolution of CNNs in image processing, covering classical and deep neural networks, training algorithms, backpropagation, non-linear steps, loss functions, and software frameworks.
Explains the learning process in multi-layer neural networks, including back-propagation, activation functions, weights update, and error backpropagation.
Explores Stochastic Gradient Descent and Mean Field Analysis in two-layer neural networks, emphasizing their iterative processes and mathematical foundations.