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Python Deep Learning Projects

You're reading from   Python Deep Learning Projects 9 projects demystifying neural network and deep learning models for building intelligent systems

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781788997096
Length 472 pages
Edition 1st Edition
Languages
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Authors (3):
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Rahul Kumar Rahul Kumar
Author Profile Icon Rahul Kumar
Rahul Kumar
Matthew Lamons Matthew Lamons
Author Profile Icon Matthew Lamons
Matthew Lamons
Abhishek Nagaraja Abhishek Nagaraja
Author Profile Icon Abhishek Nagaraja
Abhishek Nagaraja
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Toc

Table of Contents (17) Chapters Close

Preface 1. Building Deep Learning Environments FREE CHAPTER 2. Training NN for Prediction Using Regression 3. Word Representation Using word2vec 4. Building an NLP Pipeline for Building Chatbots 5. Sequence-to-Sequence Models for Building Chatbots 6. Generative Language Model for Content Creation 7. Building Speech Recognition with DeepSpeech2 8. Handwritten Digits Classification Using ConvNets 9. Object Detection Using OpenCV and TensorFlow 10. Building Face Recognition Using FaceNet 11. Automated Image Captioning 12. Pose Estimation on 3D models Using ConvNets 13. Image Translation Using GANs for Style Transfer 14. Develop an Autonomous Agent with Deep R Learning 15. Summary and Next Steps in Your Deep Learning Career 16. Other Books You May Enjoy

Automating the setup process

Installing of Python packages and DL libraries can be a tedious process, requiring lots of time and repetitive effort. So, to ease the job, we will create a bash script that can be used to install everything using a single command.

The following is a list of components that will get installed and configured:

  • Java 8
  • Bazel for building
  • Python and associated dependencies
  • TensorFlow
  • Keras
  • Git
  • Unzip
  • Dependencies for all of the aforementioned services (see the script for exact details)

You can simply download the automation script to your server or locally, execute it, and you're done. Here are the steps to follow:

  1. Save the script to your home directory, by cloning the code from the repository:
git clone https://github.com/PacktPublishing/Python-Deep-Learning-Projects
  1. Once you have the copy of the complete repository, move to the Chapter01 folder, which will contain a script file named setupDeepLearning.sh. This is the script that we will execute to start the setup process, but, before execution, we will have to make it executable using the chmod command:
cd Python-Deep-Learning-Projects/Chapter01/
chmod +x setupDeepLearning.sh
    1. Once this is done, we are ready to execute it as follows:
    ./setupDeepLearning.sh

    Follow any instructions that appear (basically, say yes to everything and accept Java's license). It should take about 10 to 15 minutes to install everything. Once it has finished, you will see the list of Python packages being installed, as shown in the following screenshot:

    Listed packages with TensorFlow and other Python dependencies

    There are a couple of other options, too, such as getting Docker images from TensorFlow and other DL packages, which can set up fully functional DL machines for large-scale and production-ready environments. You can find out more about Docker at https://www.docker.com/what-docker. Also, for a quick-start guide, follow the instructions on this repository for an all-in-one Docker image for DL at https://github.com/floydhub/dl-docker.

    You have been reading a chapter from
    Python Deep Learning Projects
    Published in: Oct 2018
    Publisher: Packt
    ISBN-13: 9781788997096
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