Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Delves into the impact of deep learning on non-conceptual knowledge systems and the advancements in transformers and generative adversarial networks.
Explores supervised learning as an ill-posed problem and the integration of sparse adaptive splines into neural architectures.
