Lecture

Natural Language Processing: Understanding Transformers and Tokenization

Related lectures (32)

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Transformers: Pretraining and Decoding Techniques

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Delves into Deep Learning for Natural Language Processing, exploring Neural Word Embeddings, Recurrent Neural Networks, and Attentive Neural Modeling with Transformers.

Transformers: Revolutionizing Attention Mechanisms in NLP

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Introduction to Modern Natural Language Processing

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

Binary Sentiment Classifier Training

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Coreference Resolution

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Language Models: Fixed-context and Recurrent Neural Networks

Discusses language models, focusing on fixed-context neural models and recurrent neural networks.

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

Natural Language Processing: A Primer

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

Language Models: From Theory to Computation

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.

Deep Learning for Question Answering

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

Non conceptual knowledge systems

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Multilingual NLP: Challenges and Innovations

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Deep Learning for NLP

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

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Explores the attention mechanism in machine translation, addressing the bottleneck problem and improving NMT performance significantly.