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Hands-On GPU Computing with Python

You're reading from  Hands-On GPU Computing with Python

Product type Book
Published in May 2019
Publisher Packt
ISBN-13 9781789341072
Pages 452 pages
Edition 1st Edition
Languages
Author (1):
Avimanyu Bandyopadhyay Avimanyu Bandyopadhyay
Profile icon Avimanyu Bandyopadhyay

Table of Contents (17) Chapters

Preface 1. Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware
2. Introducing GPU Computing 3. Designing a GPU Computing Strategy 4. Setting Up a GPU Computing Platform with NVIDIA and AMD 5. Section 2: Hands-On Development with GPU Programming
6. Fundamentals of GPU Programming 7. Setting Up Your Environment for GPU Programming 8. Working with CUDA and PyCUDA 9. Working with ROCm and PyOpenCL 10. Working with Anaconda, CuPy, and Numba for GPUs 11. Section 3: Containerization and Machine Learning with GPU-Powered Python
12. Containerization on GPU-Enabled Platforms 13. Accelerated Machine Learning on GPUs 14. GPU Acceleration for Scientific Applications Using DeepChem 15. Other Books You May Enjoy Appendix A

Accelerated Machine Learning on GPUs

In this chapter, we begin a new discussion with machine learning through GPU-enabled Python. The end objective of these chapters is to encourage the user to develop applications to benefit the scientific AI community. The fundamental steps to write a machine learning-based program will be illustrated via use cases.

With the help of the use cases, we will establish how GPU-enabled Python and machine learning can work in tandem to facilitate processing and analysis of large datasets. We will look at the significance of big data management, deep learning, and other crucial concepts. Additionally, computational exercises will be revisited but with a machine learning approach. The solution assistance section will help you devise your own techniques to implement machine learning on the three unique problems discussed previously through chapters...

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