Internship Report: Hangqian Li

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Related lectures (32)

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Deep Learning: Principles and Applications

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Transformers in Vision: Applications and Architectures

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

Transformers: Unifying Machine Learning Communities

Covers the role of Transformers in unifying various machine learning fields.

Classical Language Models: Foundations and Applications

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

Introduction to Modern Natural Language Processing

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

Natural Language Processing: A Primer

Introduces Natural Language Processing (NLP) and its applications, covering tokenization, machine learning, sentiment analysis, and Swiss NLP applications.

Natural Language Processing: Understanding Transformers and Tokenization

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

Introduction to Financial Markets and Time Series

Introduces financial markets, time series, machine learning applications in finance, and natural language processing.

Ethics in NLP

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

Foundations of Information Systems: Course Overview and Key Concepts

Introduces the course on information systems, covering its structure, objectives, and foundational concepts essential for understanding data management and decision-making.