Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.
Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Delves into the impact of deep learning on non-conceptual knowledge systems and the advancements in transformers and generative adversarial networks.
Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Explores neural model analysis in NLP, covering evaluation, probing, and ablation studies to understand model behavior and interpretability.
Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
