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 variance reduction techniques in deep learning, covering gradient descent, stochastic gradient descent, SVRG method, and performance comparison of algorithms.
