Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
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.
Introduces Long Short-Term Memory (LSTM) networks as a solution to vanishing and exploding gradients in recurrent neural networks.
Explores Recurrent Neural Networks for behavioral data, covering Deep Knowledge Tracing, LSTM, GRU networks, hyperparameter tuning, and time series prediction tasks.
Explores Deep Knowledge Tracing and its application in predicting student learning outcomes using neural networks and loss functions.
