Predicting Reaction Yields with Deep Learning

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

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Explores the role of graphs in deep learning, focusing on their structure, applications, and techniques for processing graph data.

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

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

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Graph Neural Networks: Interconnected World

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