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Managing Data Science

You're reading from   Managing Data Science Effective strategies to manage data science projects and build a sustainable team

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Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781838826321
Length 290 pages
Edition 1st Edition
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Author (1):
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Kirill Dubovikov Kirill Dubovikov
Author Profile Icon Kirill Dubovikov
Kirill Dubovikov
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Table of Contents (18) Chapters Close

1. Section 1: What is Data Science? FREE CHAPTER
2. What You Can Do with Data Science 3. Testing Your Models 4. Understanding AI 5. Section 2: Building and Sustaining a Team
6. An Ideal Data Science Team 7. Conducting Data Science Interviews 8. Building Your Data Science Team 9. Section 3: Managing Various Data Science Projects
10. Managing Innovation 11. Managing Data Science Projects 12. Common Pitfalls of Data Science Projects 13. Creating Products and Improving Reusability 14. Section 4: Creating a Development Infrastructure
15. Implementing ModelOps 16. Building Your Technology Stack 17. Conclusion 18. Other Books You May Enjoy

Exploring deep learning

Deep neural networks that classify images and play Go better than we do create an impression of extremely complex models whose internals are inspired by our own brain's structure. In fact, the central ideas behind neural networks are easy to grasp. While first neural networks were indeed inspired by the physical structure of our brain, the analogy no longer holds and the relation to physical processes inside the human brain is mostly historical.

To demystify neural networks, we will start with the basic building blocks: artificial neurons. An artificial neuron is nothing more than two mathematical functions. The first takes a bunch of numbers as input and combines them by using its internal state—weights. The second, an activation function, takes the output of the first and applies special transformations. The activation function tells us how...

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