Language Models: From Theory to Computation

Related lectures (30)

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Covers advanced transformer architectures in deep learning, focusing on Swin, HUBERT, and Flamingo models for multimodal applications.

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

Covers advanced transformer concepts, focusing on pretraining and decoding techniques in NLP.

Covers the pretraining of sequence-to-sequence models, focusing on BART and T5 architectures.

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Covers deep learning concepts, focusing on graphs, transformers, and their applications in multimodal data processing.

Covers contextual representations in NLP, focusing on ELMO and BERT architectures and their applications in various tasks.

Explores the impact of Deep learning on Digital Humanities, focusing on non conceptual knowledge systems and recent advancements in AI.

Covers the training of a binary sentiment classifier using an RNN.

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.