Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Explores entropy, KL divergence, and maximum entropy principle in probability models for data science.
Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
Explores entropy in thermodynamics, including Gibbs equations, ideal gases, and entropy balance in closed systems.
Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.
