Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Introduces the fundamentals of deep learning, covering neural networks, CNNs, special layers, weight initialization, data preprocessing, and regularization.
Introduces Long Short-Term Memory (LSTM) networks as a solution to vanishing and exploding gradients in recurrent neural networks.
Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
