Explores Recurrent Neural Networks for behavioral data, covering Deep Knowledge Tracing, LSTM, GRU networks, hyperparameter tuning, and time series prediction tasks.
Explores Seq2Seq models with and without attention mechanisms, covering encoder-decoder architecture, context vectors, decoding processes, and different types of attention mechanisms.
Explores the theoretical properties and practical power of Recurrent Neural Networks, including their relationship to state machines and Turing completeness.
Explores the aim and process of batch normalization in deep neural networks, emphasizing its importance in stabilizing mean input and solving the vanishing gradient problem.