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Related lectures (31)

Ethics in Natural Language Processing: Addressing Bias and Misinformation

Covers ethical considerations in NLP, focusing on bias, toxicity, and misinformation.

Introduction to NLP and the Course

Covers the basics of Natural Language Processing, including challenges, linguistic processing levels, and the impact of power laws.

Deep Learning for NLP

Explores deep learning for NLP, covering word embeddings, context representations, learning techniques, and challenges like vanishing gradients and ethical considerations.

Data-Driven Insights: NLP and AI Applications

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

Ethics in NLP

Discusses the ethical implications of NLP systems, focusing on biases, toxicity, and privacy concerns in language models.

Contextual Representations: ELMO and BERT Overview

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

Efficient Methods in Natural Language Processing

Explores the efficiency of training large language models and the environmental impact of AI.

Natural Language Generation: Evaluating Text Quality

Covers the evaluation methods for natural language generation systems, including metrics and human assessments of generated text quality.

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.

Deep Learning: Exploring Vision and Language Transformers

Covers advanced transformer architectures in deep learning, focusing on Swin, HUBERT, and Flamingo models for multimodal applications.

Scaling Language Models: Efficiency and Deployment

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

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