Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.
Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
Covers neuromorphic computing, challenges in ternary and binary computing, hardware simulations of the brain, and new materials for artificial brain cells.
Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Discusses the challenges and future of neuromorphic computing, comparing digital computers and specialized hardware, such as SpiNNaker and NEST, while exploring the Human Brain Project's Neuromorphic Computing Platform.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.