Pre-Training: BiLSTM and Transformer

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Covers sequence to sequence models, their architecture, applications, and the role of attention mechanisms in improving performance.

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

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

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

Delves into BERT pretraining for transformers, discussing its applications in NLP tasks.

Explores language modelling, RNNs, n-gram models, LSTMs, and bidirectional RNNs.

Explores prompting, alignment, and the capabilities of large language models for natural language processing tasks.

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

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