Lectures related to From Attention to Transformers

Transformers: Pretraining and Decoding Techniques

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

Sequence to Sequence Models: Overview and Applications

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

Transformers: Revolutionizing Attention Mechanisms in NLP

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

Transformer Architecture: The X Gomega

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

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.

Contextual Representations: ELMO and BERT Overview

Covers contextual representations in NLP, focusing on ELMO and BERT architectures and their applications in various 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.

Deep Learning for NLP Log in to Mediaspace to watch this video

Introduces deep learning concepts for NLP, covering word embeddings, RNNs, and Transformers, emphasizing self-attention and multi-headed attention.

Machine Translation: Attention Mechanism Log in to Mediaspace to watch this video

Explores the attention mechanism in machine translation, addressing the bottleneck problem and improving NMT performance significantly.

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.

Deep Generative Models: Part 2 Log in to Mediaspace to watch this video

Explores deep generative models, including mixtures of multinomials, PCA, deep autoencoders, convolutional autoencoders, and GANs.

Untitled Log in to Mediaspace to watch this video

Deep Learning for NLP Log in to Mediaspace to watch this video

Delves into Deep Learning for Natural Language Processing, exploring Neural Word Embeddings, Recurrent Neural Networks, and Attentive Neural Modeling with Transformers.

Transformers: Unifying Machine Learning Communities Log in to Mediaspace to watch this video

Covers the role of Transformers in unifying various machine learning fields.

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.

Transformers in Vision: Applications and Architectures Log in to Mediaspace to watch this video

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

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.

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

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

Neural Networks for NLP Log in to Mediaspace to watch this video

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.