Lectures related to Natural Language Generation: Evaluation

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Deep Generative Models: Part 2

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

Interpretability & Analysis: Modern NLP

Explores model interpretability in NLP, focusing on probing methods and local explanations to understand model predictions.

Natural Language Generation: Decoding Techniques and Training Challenges

Covers decoding methods and training challenges in natural language generation.

Pretraining: Transformers & Models

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

Natural Language Generation: Task

On NLG covers basics, autoregressive models, decoding methods, and challenges in text generation.

Modern NLP: From GPT to ChatGPT

Explores the evolution of modern NLP from GPT-2 to GPT-3, emphasizing in-context learning and the development of ChatGPT.

Handling Text Data: Document Retrieval and Classification

Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors.

Unit Conversion: SI to cgs

Explains the conversion between SI and cgs units for magnetic fields and magnetization, highlighting the importance of accurate unit conversions in scientific calculations.

Natural Language Processing Basics

Covers the basics of Natural Language Processing, including tokenization, part-of-speech tagging, and embeddings, and explores practical applications like sentiment analysis.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Predicting Reaction Yields with Deep Learning

Explores predicting reaction yields with deep learning models and the importance of high-quality data sets in chemistry.

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.

Data-Driven Insights: NLP and AI Applications

Explores building OS for heterogeneous hardware, data movement efficiency, AI advancements, and NLP challenges.

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.

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

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

Natural Language Processing: Understanding Transformers and Tokenization Log in to Mediaspace to watch this video

Provides an overview of Natural Language Processing, focusing on transformers, tokenization, and self-attention mechanisms for effective language analysis and synthesis.

Language Models: From Theory to Computation

Explores the mathematics of language models, covering architecture design, pre-training, and fine-tuning, emphasizing the importance of pre-training and fine-tuning for various tasks.

Introduction to Natural Language Processing Log in to Mediaspace to watch this video

Introduces the basics of Natural Language Processing, covering challenges, application domains, and linguistic processing levels.

Text Understanding

Explores Text Understanding, focusing on Named Entities, Information Extraction, and Machine Reading methods.

Natural Language Generation: Evaluation

Chat with Graph Search