BERT: Pretraining and Applications

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Related lectures (31)

Deep Learning for Question Answering

Explores deep learning for question answering, analyzing neural networks and model robustness to noise.

Language Models: Fixed-context and Recurrent Neural Networks

Discusses language models, focusing on fixed-context neural models and recurrent neural networks.

Ethics in NLP

Discusses the ethical implications of NLP systems, focusing on biases, toxicity, and privacy concerns in language models.

Coreference Resolution

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

Model Compression: Techniques for Efficient NLP Models

Explores model compression techniques in NLP, discussing pruning, quantization, weight factorization, knowledge distillation, and attention mechanisms.

Vision-Language-Action Models: Training and Applications

Delves into training and applications of Vision-Language-Action models, emphasizing large language models' role in robotic control and the transfer of web knowledge. Results from experiments and future research directions are highlighted.

Modern NLP: From GPT to ChatGPT

Explores the evolution of modern NLP from GPT-2 to GPT-3, emphasizing in-context learning and the development of ChatGPT.

Question Answering: Deep Learning Insights

Explores question answering systems, reading comprehension models, and the challenges in achieving accurate responses.

Contextual Representations: ELMo & BERT

Explores the development of contextualized embeddings in NLP, focusing on ELMo and BERT's advancements and impact on NLP tasks.

Transformers: Pre-Training

Discusses challenges and advancements in Transformers, pretraining models, and subword tokenization in NLP.

Transformers: Full Architecture and Self-Attention Mechanism

Explains the full architecture of Transformers and the self-attention mechanism, highlighting the paradigm shift towards using completely pretrained models.

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