Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.
Delves into the geometric insights of deep learning models, exploring their vulnerability to perturbations and the importance of robustness and interpretability.
Explores the influence of computational linguistics on deep learning architectures, covering grammar formalisms, connectionism, variable binding, and future directions.
Delves into the challenges and benefits of deep learning, highlighting the transition to convolutional neural networks and the impact of network width on the loss landscape.