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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

The Iris dataset

We will now construct our very own DNN for a real-life problem: classification of flower types based on the measurements of petals. We will be working with the well-known Iris dataset for this. This dataset is stored as a comma-separated value (CSV) text file, with each line containing four different numerical values (petal measurements), followed by the flower type (here, there are three classes—Irissetosa, Irisversicolor, and Irisvirginica). We will now design a small DNN that will classify the type of iris, based on this set.

Before we continue, please download the Iris dataset and put it into your working directory. This is available from the UC Irvine Machine Learning repository, which can be found here: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data.

We will start by processing this file into appropriate data arrays that...

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