Explores neural model analysis in NLP, covering evaluation, probing, and ablation studies to understand model behavior and interpretability.
Introduces Long Short-Term Memory (LSTM) networks as a solution to vanishing and exploding gradients in recurrent neural networks.
Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.
Delves into the impact of deep learning on non-conceptual knowledge systems and the advancements in transformers and generative adversarial networks.
Explores deep generative models, including mixtures of multinomials, PCA, deep autoencoders, convolutional autoencoders, and GANs.
Introduces neural word embeddings and dense vector representations for natural language processing.
