Natural Language Generation

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Related lectures (32)

Page 2 of 4

Introduction to Modern Natural Language Processing

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

Modern NLP: Classical Language Models

Covers language models, n-gram models, smoothing techniques, and fixed-context neural models.

Deep Learning for Question Answering

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

Coreference Resolution

Covers coreference resolution, models, applications, challenges, and advancements in natural language processing.

Modern NLP: A Simple Neural Classifier

Introduces the basics of modern NLP, covering word embeddings, neural models, and tasks like sequence labeling and text generation.

Transformers: Revolutionizing Attention Mechanisms in NLP

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

Modern NLP: Introduction

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

Neural Networks for NLP

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

Neuro-symbolic Representations: Commonsense Knowledge & Reasoning

Explores neuro-symbolic representations for understanding commonsense knowledge and reasoning, emphasizing the challenges and limitations of deep learning in natural language processing.

Classical Language Models: Foundations and Applications

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