Covers the Boltzmann Machine, a type of stochastic recurrent neural network.
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.
Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.
Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Offers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.
