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Controlled natural language
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Related lectures (32)
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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.
Efficient Methods in Natural Language Processing
Explores the efficiency of training large language models and the environmental impact of AI.
Deep Learning: Principles and Applications
Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.
Coreference Resolution
Covers coreference resolution, models, applications, challenges, and advancements in natural language processing.
Data-Driven Insights: NLP and AI Applications
Explores building OS for heterogeneous hardware, data movement efficiency, AI advancements, and NLP challenges.
NLP Pre-processing: Tokenization, Stop Words, Lemmatization
Covers tokenization, stop words removal, and lemmatization for NLP tasks.
Neuro-symbolic Representations: Commonsense Knowledge & Reasoning
Delves into neuro-symbolic representations for commonsense knowledge and reasoning in natural language processing applications.
Compositional Representations and Systematic Generalization
Examines systematicity, compositionality, neural network challenges, and unsupervised learning in NLP.
Words and Tokens: Language Models and Probabilities
Reviews language models, tokenization, and probability estimation in NLP systems.
Classical Language Models: Foundations and Applications
Introduces classical language models, their applications, and foundational concepts like count-based modeling and evaluation metrics.
Scaling Language Models: Efficiency and Deployment
Covers the scaling of language models, focusing on training efficiency and deployment considerations.
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.
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