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Machine Learning Yearning
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  • Title Machine Learning Yearning
  • Author(s) Andrew Ng
  • Publisher: GitHub; eBook (Draft, 2018)
  • Paperback N/A
  • eBook PDF (118 pages)
  • Language: English
  • ISBN-10: 199957950X
  • ISBN-13: 978-1999579500
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Book Description

In this book you will learn how to align on ML strategies in a team setting, as well as how to set up development (dev) sets and test sets. Recommendations for how to set up dev/test sets have been changing as Machine Learning is moving toward bigger datasets, and this explains how you should do it for modern ML projects.

After finishing this book, you will have a deep understanding of how to set technical direction for a machine learning project. But your teammates might not understand why you're recommending a particular direction. Perhaps you want your team to define a single-number evaluation metric, but they aren’t convinced. How do you persuade them? That's why I made the chapters short: So that you can print them out and get your teammates to read just the 1-2 pages you need them to know. A few changes in prioritization can have a huge effect on your team’s productivity. By helping your team with a few such changes, I hope that you can become the superhero of your team!

AI, machine learning, and deep learning are transforming numerous industries. But building a machine learning system requires that you make practical decisions:

  • Should you collect more training data?
  • Should you use end-to-end deep learning?
  • How do you deal with your training set not matching your test set?

and many more.

Historically, the only way to learn how to make these "strategy" decisions has been a multi-year apprenticeship in a graduate program or company. This is a book to help you quickly gain this skill, so that you can become better at building AI systems.

About the Authors
  • Andrew Ng is a British-born American businessman, computer scientist, investor, and writer. He is focusing on machine learning and AI.[2] As a businessman and investor, Ng co-founded and led Google Brain and was a former Vice President and Chief Scientist at Baidu, building the company's Artificial Intelligence Group into a team of several thousand people.
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