Natural language processing using transformer architectures:
Whether you need to automatically judge the sentiment of a user review, summarize long documents, translate text, or build a chatbot, you need the best language model available. In 2018, pretty much every NLP benchmark was crushed by novel transformer-based architectures, replacing long-standing arc...
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| Corporate Author: | |
| Format: | Electronic Video |
| Language: | English |
| Published: |
[Erscheinungsort nicht ermittelbar]
O'Reilly Media, Inc.
2020
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| Edition: | 1st edition. |
| Links: | https://learning.oreilly.com/library/view/-/0636920373605/?ar |
| Summary: | Whether you need to automatically judge the sentiment of a user review, summarize long documents, translate text, or build a chatbot, you need the best language model available. In 2018, pretty much every NLP benchmark was crushed by novel transformer-based architectures, replacing long-standing architectures based on recurrent neural networks. In short, if you're into NLP, you need transformers. But to use transformers, you need to know what they are, what transformer-based architectures look like, and how you can implement them in your projects. Aurélien Géron (Kiwisoft) dives into recurrent neural networks and their limits, the invention of the transformer, attention mechanisms, the transformer architecture, subword tokenization using SentencePiece, self-supervised pretraining-learning from huge corpora, one-size-fits-all language models, BERT and GPT 2, and how to use these language models in your projects using TensorFlow. What you'll learn Understand transformers and modern language models and how they can tackle complex NLP tasks Identify what tools to use and what the code looks like. |
| Item Description: | Online resource; Title from title screen (viewed February 28, 2020) |
| Physical Description: | 1 Online-Ressource (1 video file, aSeitenSeitenroximately 45 min.) |
| Format: | Mode of access: World Wide Web. |
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| spelling | Géron, Aurélien VerfasserIn aut Natural language processing using transformer architectures Géron, Aurélien 1st edition. [Erscheinungsort nicht ermittelbar] O'Reilly Media, Inc. 2020 Boston, MA Safari. 1 Online-Ressource (1 video file, aSeitenSeitenroximately 45 min.) zweidimensionales bewegtes Bild tdi rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Online resource; Title from title screen (viewed February 28, 2020) Whether you need to automatically judge the sentiment of a user review, summarize long documents, translate text, or build a chatbot, you need the best language model available. In 2018, pretty much every NLP benchmark was crushed by novel transformer-based architectures, replacing long-standing architectures based on recurrent neural networks. In short, if you're into NLP, you need transformers. But to use transformers, you need to know what they are, what transformer-based architectures look like, and how you can implement them in your projects. Aurélien Géron (Kiwisoft) dives into recurrent neural networks and their limits, the invention of the transformer, attention mechanisms, the transformer architecture, subword tokenization using SentencePiece, self-supervised pretraining-learning from huge corpora, one-size-fits-all language models, BERT and GPT 2, and how to use these language models in your projects using TensorFlow. What you'll learn Understand transformers and modern language models and how they can tackle complex NLP tasks Identify what tools to use and what the code looks like. Mode of access: World Wide Web. Safari, an O'Reilly Media Company. MitwirkendeR ctb |
| spellingShingle | Géron, Aurélien Natural language processing using transformer architectures |
| title | Natural language processing using transformer architectures |
| title_auth | Natural language processing using transformer architectures |
| title_exact_search | Natural language processing using transformer architectures |
| title_full | Natural language processing using transformer architectures Géron, Aurélien |
| title_fullStr | Natural language processing using transformer architectures Géron, Aurélien |
| title_full_unstemmed | Natural language processing using transformer architectures Géron, Aurélien |
| title_short | Natural language processing using transformer architectures |
| title_sort | natural language processing using transformer architectures |
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