Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Building Data Science Solutions with Anaconda

You're reading from   Building Data Science Solutions with Anaconda A comprehensive starter guide to building robust and complete models

Arrow left icon
Product type Paperback
Published in May 2022
Publisher Packt
ISBN-13 9781800568785
Length 330 pages
Edition 1st Edition
Arrow right icon
Author (1):
Arrow left icon
Dan Meador Dan Meador
Author Profile Icon Dan Meador
Dan Meador
Arrow right icon
View More author details
Toc

Table of Contents (16) Chapters Close

Preface 1. Part 1: The Data Science Landscape – Open Source to the Rescue
2. Chapter 1: Understanding the AI/ML landscape FREE CHAPTER 3. Chapter 2: Analyzing Open Source Software 4. Chapter 3: Using the Anaconda Distribution to Manage Packages 5. Chapter 4: Working with Jupyter Notebooks and NumPy 6. Part 2: Data Is the New Oil, Models Are the New Refineries
7. Chapter 5: Cleaning and Visualizing Data 8. Chapter 6: Overcoming Bias in AI/ML 9. Chapter 7: Choosing the Best AI Algorithm 10. Chapter 8: Dealing with Common Data Problems 11. Part 3: Practical Examples and Applications
12. Chapter 9: Building a Regression Model with scikit-learn 13. Chapter 10: Explainable AI - Using LIME and SHAP 14. Chapter 11: Tuning Hyperparameters and Versioning Your Model 15. Other Books You May Enjoy

Evaluating a new tool or library

The only constant is change, and there is no doubt as I type this, a new tool that "fixes" all the things that are broken with framework X but is simpler to use is being developed. This section will help you navigate the new world where a constant stream of new software is available for free. You will learn what attributes and factors to look at to decide whether something is worth using or not.

There are a few heuristics that you could use when evaluating a new tool. Feel free to adjust which ones you use based on your specific needs:

  • The number of stars the tool has on GitHub
  • The tool's age
  • How long it's been since the tool has been updated
  • The number of maintainers
  • The number of open issues/PRs
  • The number of dependencies

I want to add a big asterisk to all of these. The answer to how important each of these are is the same as the answer to which architecture style is right for your code base...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime