Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.
Explores supervised learning as an ill-posed problem and the integration of sparse adaptive splines into neural architectures.
Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
