Covers the preparation for deriving the Backprop algorithm in layered networks using multi-layer perceptrons and gradient descent.
Explores neural networks, hidden layers, weight adjustments, activation functions, and the universal approximation theorem.
Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.
Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
