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Hands-On GPU Programming with Python and CUDA

You're reading from   Hands-On GPU Programming with Python and CUDA Explore high-performance parallel computing with CUDA

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788993913
Length 310 pages
Edition 1st Edition
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Author (1):
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Dr. Brian Tuomanen Dr. Brian Tuomanen
Author Profile Icon Dr. Brian Tuomanen
Dr. Brian Tuomanen
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Table of Contents (15) Chapters Close

Preface 1. Why GPU Programming? 2. Setting Up Your GPU Programming Environment FREE CHAPTER 3. Getting Started with PyCUDA 4. Kernels, Threads, Blocks, and Grids 5. Streams, Events, Contexts, and Concurrency 6. Debugging and Profiling Your CUDA Code 7. Using the CUDA Libraries with Scikit-CUDA 8. The CUDA Device Function Libraries and Thrust 9. Implementation of a Deep Neural Network 10. Working with Compiled GPU Code 11. Performance Optimization in CUDA 12. Where to Go from Here 13. Assessment 14. Other Books You May Enjoy

Machine learning and computer vision

Of course, the elephant in the room of this chapter is machine learning and its fraternal twin computer vision. It goes without saying that machine learning (particularly the subfields of deep neural networks and convolutional neural networks) is what is keeping a roof over NVIDIA CEO Jensen Huang's head these days. (Okay, we admit that was the understatement of the decade...) If you need a reminder as to why GPUs are so applicable and useful in this field, please take another look at Chapter 9, Implementation of a Deep Neural Network. A large number of parallel computations and mathematical operations, as well as the user-friendly mathematical libraries, have made NVIDIA GPUs the hardware backbone of the machine learning industry.

The basics...

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