Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.
Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
