Internship Report: Hangqian Li

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Related lectures (32)

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Pretraining Sequence-to-Sequence Models: BART and T5

Covers the pretraining of sequence-to-sequence models, focusing on BART and T5 architectures.

Scaling Language Models: Efficiency and Deployment

Covers the scaling of language models, focusing on training efficiency and deployment considerations.

Introduction to Natural Language Processing

Covers the basics of Natural Language Processing, from traditional to modern approaches, highlighting the challenges and importance of studying both methods.

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.

Music Streaming Research: Natural Language Processing at Deezer

Showcases a student's internship at Deezer's research team working on natural language processing.

Language Models: Fixed-context and Recurrent Neural Networks

Discusses language models, focusing on fixed-context neural models and recurrent neural networks.

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.

Data Annotation: Collection and Biases in NLP

Addresses data collection, annotation processes, and biases in natural language processing.

Model Analysis

Explores neural model analysis in NLP, covering evaluation, probing, and ablation studies to understand model behavior and interpretability.

Deep Learning for Question Answering

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