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Language Models In Plain English Humans Understanding How Machines Understand Language Austin Eovito Marina Danilevsky

Name: Language Models In Plain English Humans Understanding How Machines Understand Language Austin Eovito Marina Danilevsky
SKU: BELL-35504226
Price: 31.00 USD
Availability: InStock
Rating: 4.25 (18 reviews)

SKU: BELL-35504226

$ 31.00 ~~$ 45.00~~ (-31%)

4.3

18 reviews

Language Models In Plain English Humans Understanding How Machines Understand Language Austin Eovito Marina Danilevsky instant download after payment.

Publisher: O'Reilly Media, Inc.

File Extension: EPUB

File size: 5.35 MB

Pages: 76

Author: Austin Eovito & Marina Danilevsky

ISBN: 9781098109066, 1098109066

Language: English

Year: 2021

Product desciption

Language Models In Plain English Humans Understanding How Machines Understand Language Austin Eovito Marina Danilevsky by Austin Eovito & Marina Danilevsky 9781098109066, 1098109066 instant download after payment.

Recent advances in machine learning have lowered the barriers to creating and using ML models. But understanding what these models are doing has only become more difficult. We discuss technological advances with little understanding of how they work and struggle to develop a comfortable intuition for new functionality.
In this report, authors Austin Eovito and Marina Danilevsky from IBM focus on how to think about neural network-based language model architectures. They guide you through various models (neural networks, RNN/LSTM, encoder-decoder, attention/transformers) to convey a sense of their abilities without getting entangled in the complex details. The report uses simple examples of how humans approach language in specific applications to explore and compare how different neural network-based language models work.
This report will empower you to better understand how machines understand language.
• Dive deep into the basic task of a language model to predict the next word, and use it as a lens to understand neural network language models
• Explore encoder-decoder architecture through abstractive text summarization
• Use machine translation to understand the attention mechanism and transformer architecture
• Examine the current state of machine language understanding to discern what these language models are good at and their risks and weaknesses