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Language Models: From Theory to Computation
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Related lectures (30)
Pre-Training: BiLSTM and Transformer
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Delves into pre-training BiLSTM and Transformer models for NLP tasks, showcasing their effectiveness and applications.
Deep Learning for NLP
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Introduces deep learning concepts for NLP, covering word embeddings, RNNs, and Transformers, emphasizing self-attention and multi-headed attention.
Chemical Reactions: Transformer Architecture
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Explores atom mapping in chemical reactions and the transition to reaction grammar using the transformer architecture.
Deep Learning for NLP
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Delves into Deep Learning for Natural Language Processing, exploring Neural Word Embeddings, Recurrent Neural Networks, and Attentive Neural Modeling with Transformers.
Deep Learning: Exploring Vision and Language Transformers
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Covers advanced transformer architectures in deep learning, focusing on Swin, HUBERT, and Flamingo models for multimodal applications.
Financial Time Series Analysis
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Deep Learning: Graphs and Transformers Overview
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Improving Models of the Ventral Visual Pathway
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Neural Networks: Training and Optimization
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Deep Generative Models: Part 2
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