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Python Deep Learning Cookbook

You're reading from   Python Deep Learning Cookbook Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781787125193
Length 330 pages
Edition 1st Edition
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Author (1):
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Indra den Bakker Indra den Bakker
Author Profile Icon Indra den Bakker
Indra den Bakker
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Table of Contents (15) Chapters Close

Preface 1. Programming Environments, GPU Computing, Cloud Solutions, and Deep Learning Frameworks 2. Feed-Forward Neural Networks FREE CHAPTER 3. Convolutional Neural Networks 4. Recurrent Neural Networks 5. Reinforcement Learning 6. Generative Adversarial Networks 7. Computer Vision 8. Natural Language Processing 9. Speech Recognition and Video Analysis 10. Time Series and Structured Data 11. Game Playing Agents and Robotics 12. Hyperparameter Selection, Tuning, and Neural Network Learning 13. Network Internals 14. Pretrained Models

Setting up a deep learning environment

Before we get started with training deep learning models, we need to set up our deep learning environment. While it is possible to run deep learning models on CPUs, the speed achieved with GPUs is significantly higher and necessary when running deeper and more complex models.

How to do it...

  1. First, you need to check whether you have access to a CUDA-enabled NVIDIA GPU on your local machine. You can check the overview at https://developer.nvidia.com/cuda-gpus.
  2. If your GPU is listed on that page, you can continue installing CUDA and cuDNN if you haven't done that already. Follow the steps in the Installing CUDA and cuDNN section.
  1. If you don't have access to an NVIDIA GPU on your local machine, you can decide to use a cloud solution. Follow the steps in the Launching a cloud solution section.
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