Transformer (machine learning model)

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Related lectures (30)

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Transformers: Pre-Training

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

Chemical Reactions: Transformer Architecture

Explores atom mapping in chemical reactions and the transition to reaction grammar using the transformer architecture.

Pretraining Sequence-to-Sequence Models: BART + T5

Explores pretraining sequence-to-sequence models with BART and T5, discussing transfer learning, fine-tuning, model architectures, tasks, performance comparison, summarization results, and references.

Transformers: Revolutionizing Attention Mechanisms in NLP

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

BERT: Pretraining and Applications

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

Graph-to-Graph Transformers: Syntax-aware Graph Encoding

Introduces the Syntax-aware Graph-to-Graph Transformer architecture for effective conditioning on syntactic dependency graphs.

Foundations of Deep Learning: Transformer Architecture Overview

Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.

Transformers: Self-Attention and MLP

Explores transformers, emphasizing self-attention and MLP mechanisms for efficient sequence processing.

Cognitive Maps in Rats and Men

Explores cognitive maps, reward systems, latent learning, attention mechanisms, and transformers in visual intelligence and machine learning.

Model Compression: Techniques for Efficient NLP Models

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