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Concept

Attention (machine learning)

Related lectures (31)

From Attention to Transformers

Explores the evolution from attention mechanisms to transformers in modern NLP, emphasizing the significance of self-attention and cross-attention.

Generative Models: Self-Attention and Transformers

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Seq2Seq Models: Attention vs. No Attention

Explores Seq2Seq models with and without attention mechanisms, covering encoder-decoder architecture, context vectors, decoding processes, and different types of attention mechanisms.

Transformers: Pretraining and Decoding Techniques

Covers advanced transformer concepts, focusing on pretraining and decoding techniques in NLP.

Transformer Architecture: The X Gomega

Delves into the Transformer architecture, self-attention, and training strategies for machine translation and image recognition.

Sequence to Sequence Models: Overview and Attention Mechanisms

Explores sequence to sequence models, attention mechanisms, and their role in addressing model limitations and improving interpretability.

Transformers: Overview and Self-Attention

Provides an overview of Transformers, self-attention, multi-headed attention, and the Transformer decoder and encoder.

Sequence to Sequence Models: Overview and Applications

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

Model Compression: Techniques for Efficient NLP Models

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

Machine Learning in Human Rights: HURIDOCS

Explores machine learning in human rights, focusing on defining goals, handling false positives and negatives, and ensuring transparency and trust.

Pretraining: Transformers & Models

Explores pretraining models like BERT, T5, and GPT, discussing their training objectives and applications in natural language processing.

Transformers: Revolutionizing Attention Mechanisms in NLP

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

Introduction to Machine Learning

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

Transformer Networks: Self-Attention

Explains Transformer networks and self-attention layers for mapping inputs and multi-head attention.

Decoding from Neural Models

Explores decoding from neural models in modern NLP, covering encoder-decoder models, decoding algorithms, issues with argmax decoding, and the impact of beam size.

Transformers in Vision Log in to Mediaspace to watch this video

Explores the evolution of visual intelligence models, focusing on Transformers and their applications in computer vision and natural language processing.

Foundations of Deep Learning: Transformer Architecture Overview Log in to Mediaspace to watch this video

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 Log in to Mediaspace to watch this video

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

Cognitive Maps in Rats and Men Log in to Mediaspace to watch this video

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

Transformer: Pre-Training Log in to Mediaspace to watch this video

Explores the Transformer model, from recurrent models to attention-based NLP, highlighting its key components and significant results in machine translation and document generation.