Explores a first-order Markov model using a sunny-rainy source example, demonstrating how past events influence future outcomes.
Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.
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.
Explores the regularized cross-entropy risk in neural networks, covering training processes and challenges in deep networks.
