Offers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.
Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Covers hand pose estimation, regression techniques, and the evolution of image classification models from LeNet to VGG19.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
