Question Answering: Deep Learning Insights

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 3 of 4

Transformers: Revolutionizing Attention Mechanisms in NLP

Covers the development of transformers and their impact on attention mechanisms in NLP.

Word Embeddings: Introduction and Applications

Introduces word embeddings, explaining how they capture word meanings based on context and their applications in natural language processing tasks.

Transformers in Vision: Applications and Architectures

Covers the impact of transformers in computer vision, discussing their architecture, applications, and advancements in various tasks.

Pretraining Sequence-to-Sequence Models: BART and T5

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

Binary Sentiment Classifier Training

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

Coreference Resolution

Delves into coreference resolution, discussing challenges, advancements, and evaluation methods.

Classical Language Models: Foundations and Applications

Introduces classical language models, their applications, and foundational concepts like count-based modeling and evaluation metrics.

Scaling Language Models: Efficiency and Deployment

Covers the scaling of language models, focusing on training efficiency and deployment considerations.

Transformer Architecture: Subquadratic Attention Mechanisms

Covers transformer architecture, focusing on encoder-decoder models and subquadratic attention mechanisms for efficient processing of input sequences.

Pre-Training: BiLSTM and Transformer

Delves into pre-training BiLSTM and Transformer models for NLP tasks, showcasing their effectiveness and applications.