Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.
Covers the preparation for deriving the Backprop algorithm in layered networks using multi-layer perceptrons and gradient descent.
Introduces Multi-Layer Perceptrons (MLPs) and covers logistic regression, reformulation, gradient descent, AdaBoost, and practical applications.
Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
