This is an advanced course on natural language processing, jointly offered by Oxford University and Google DeepMind (the development agency of AlphaGo), lectured by Phil Blunsom, and invited a number of industry lecturers from DeepMind and NVIDIA to give guest lectures. This is the latest course of the 2017 Spring Semester of the Department of Computer Science, University of Oxford, which is localized by Big Data Digest.
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How to automatically process natural language input and generate language output is an important research direction of artificial intelligence. This course mainly teaches the latest development of deep learning in natural language processing in the past 2-3 years. The course starts with the mathematical principles and optimal algorithms of related machine learning models, and will cover a series of applications of neural networks in NLP, including latent semantic analysis, speech-to-text transcription, language translation, and question answering. to the implementation of these models on CPU and GPU.
All course materials (videos, lecture notes, assignments) are freely published on GitHub, https://github.com/oxford-cs-deepnlp-2017/lectures , and Big Data Digest has contacted the lecturer for translation authorization.
【Course outline】(The video content is continuously updated)
- introduction
- Word Vectors and Lexical Semantics
- Language Modeling RNN
- Text Categorization
- Nvidia RNN and GPU
- Conditional Language Modeling
- Speech Recognition
- text-to-speech
- Smart Q&A
- Memory and Linguistics
suitable for the crowd
- People who want to learn natural language processing, have basic knowledge of probability theory, linear algebra, calculus, machine learning foundation and good programming foundation
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