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

Machine learning with TensorFlow and PyTorch

Now that we have learned how to install configure TensorFlow and PyTorch, it's time to begin our hands-on experience. Before we do that, let's learn a little about the datasets we be downloading, consider another interesting piece of information.

MNIST

MNIST is a database of a large number of handwritten digits. The classic MNIST dataset is preferred by many enthusiasts when getting started with or testing machine learning systems. MNIST is very useful to develop intelligent image-processing systems. MNIST datasets are based on the MNIST database.

Fashion-MNIST

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