Chemical Reaction Prediction: Molecular Transformer

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Pre-Training: BiLSTM and Transformer

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

Transformers in Vision: Applications and Architectures

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

Introduction to Modern Natural Language Processing

Introduces the course on Modern Natural Language Processing, covering its significance, applications, challenges, and advancements in technology.

Deep Learning for NLP

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

Pretraining: Transformers & Models

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

Modern NLP: Introduction

By Antoine Bosselut introduces Natural Language Processing and its challenges, advancements in neural models, and course goals.

Classical Language Models: Foundations and Applications

Introduces classical language models, their applications, and foundational concepts like count-based modeling and evaluation metrics.

Transformers: Overview and Self-Attention

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

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

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

Contextual Representations: ELMO and BERT Overview

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