Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.
Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.
