"Scaling Python with Dask: From Data Science to Machine Learning" by Holden Karau and Mika Kimmins is considered an important guide for those working with large volumes of data and seeking to optimize their computational resources using Dask. Dask is a Python library developed for distributed data processing and machine learning.
With it, you can process huge arrays of data that don't fit into RAM, as well as scale machine learning tasks across multiple processors or servers. This manual demonstrates how to use Dask to achieve high performance with minimal time spent adapting to a new tool.
Downloading the book "Scaling Python with Dask: From Data Science to Machine Learning" by Holden Karau and Mika Kimmins right now is worth it for anyone who wants to accelerate their projects in data science and machine learning.
What are the advantages of "Scaling Python with Dask: From Data Science to Machine Learning" by Holden Karau and Mika Kimmins?
The manual provides a clear and step-by-step guide to mastering Dask for various tasks, from data processing to machine learning. The main advantage of the publication is its focus on practical application. You'll get clear instructions that can be immediately used in your projects.
Thanks to this publication, you'll be able to scale your programs without having to rewrite them for new resources. The authors also offer simple code examples that help implement the acquired knowledge faster.
What will you learn from reading the book?
After reading this guide, you'll understand how to optimize data processing and machine learning workflows using Dask. You'll learn about performance improvement methods and their application in real projects. You'll also be able to integrate Dask into existing infrastructure. Inside the manual:
- Dask basics and its architecture
- Working with large data arrays
- Parallel and distributed task execution
- Optimization of computational resources
- Scaling machine learning models
More About the Author of the Book
FAQ for "Scaling Python with Dask: From Data Science to Machine Learning"
Is the textbook suitable for beginner programmers?
The book is aimed at specialists who already have experience with Python and data analysis. Beginners may need preliminary preparation.
How does Dask help in data processing?
Dask simplifies working with large datasets by distributing tasks across multiple processors or servers, allowing faster processing of large volumes of information.
How quickly can one master Dask with this manual?
If you have experience with Python and libraries like Pandas, mastering Dask won't take long. The manual includes examples and instructions that you can apply at the early stages of reading.
Can Dask be used for machine learning?
Yes, the book explains in detail how to scale machine learning models using Dask and distributed computing.
Are additional tools required to work with Dask?
The main tools are Python and Dask. Other libraries such as NumPy, Pandas, and Scikit-learn can also be used in combination for more efficient work with data and models.
Information
| Author: | Holden Karau, Mika Kimmins | Language: | English |
| Publisher: | O'Reilly Media | ISBN-13: | 978-1098119874 |
| Publication Date: | August 22, 2023 | ISBN-10: | 1098119878 |
| Print Length: | 223 pages |
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*The book is taken from free sources and is presented for informational purposes only. The contents of the book are the intellectual property of the author and express his views. After reading, we insist on purchasing the official publication on Amazon!
