Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Explores neural model analysis in NLP, covering evaluation, probing, and ablation studies to understand model behavior and interpretability.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Explores parsing text into trees using parser combinators in Scala, covering filtering, transforming, sequencing, alternatives, recursion, spaces handling, lexing, monadic nature, and for-notation.
Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
