Open Course: Natural Language Processing
Time & Location
About the Event
1. Introduction to Natural Language Processing 2. Supervised/Unsupervised Learning 3. Basic Gradient-based Optimization 4. Language Models and Word/Sentence Embedding 5. Introduction of the PBL Subject and the Tracks
NLP is currently deployed in a tremendous number of industrial applications: from recommender systems (Amazon, Alibaba), to search engines (Google, DuckDuckGo), spam-filtering (Gmail), automatic translations or chatbots used for customer support. It is even used in the financial industry: in algorithmic trading (G-Research, Citadel) to take automatic decisions based on recent news and tweets, or as a way to extract relevant qualitative information for traders and assets managers (Kensho). Sentiment analysis is widely used to analyze customer reviews and predict the outcome of political elections. In the legal domain, NLP is used to parse contracts and run sophisticated statistics to predict the outcome of a case based on past trial data (Lexis, Ravel Law, CaseMine, etc). The availability of powerful open-source NLP frameworks (SpaCy, Gensim), task-specific libraries (e.g. TextBlob, Blackstone, Holmes) and language models (transformers, GPT-2, BERT) under permissive commercial (MIT) licenses allow everyone now more than ever to build high-impact applications quickly without requiring significant compute resources.
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