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Explores neuro-symbolic representations for understanding commonsense knowledge and reasoning, emphasizing the challenges and limitations of deep learning in natural language processing.
Introduces Natural Language Processing (NLP) and its applications, covering tokenization, machine learning, sentiment analysis, and Swiss NLP applications.
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.
Explores the influence of computational linguistics on deep learning architectures, covering grammar formalisms, connectionism, variable binding, and future directions.
