Explains the learning process in multi-layer neural networks, including back-propagation, activation functions, weights update, and error backpropagation.
Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
Explores the evolution of CNNs in image processing, covering classical and deep neural networks, training algorithms, backpropagation, non-linear steps, loss functions, and software frameworks.
Explores Stochastic Gradient Descent and Mean Field Analysis in two-layer neural networks, emphasizing their iterative processes and mathematical foundations.
