Deep Learning FundamentalsIntroduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Statistical Physics of LearningOffers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.
Model AnalysisExplores neural model analysis in NLP, covering evaluation, probing, and ablation studies to understand model behavior and interpretability.
Modeling Neuronal ActivityExplores modeling neuronal activity, including firing rates, responses to stimuli, and network behavior.
Deep Neural NetworksCovers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.
