Abstract
This book assumes that you have some basic know-how in machine learning, neural networks, and TensorFlow. It follows my first book, Applied Deep Learning: A Case-Based Approach (ISBN 978-1-4842-3790-8), published by Apress in 2018, and assumes you know and understand what is explained in there. The first volume’s goal is to explain the basic concepts of neural networks and to give you a sound basis in deep learning, and this book’s goal is to explain more advanced topics, like convolutional and recurrent neural networks. To be able to profit from this book, you should have at least a basic knowledge of the following topics:
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Notes
- 1.
TensorFlow, the TensorFlow logo, and any related marks are trademarks of Google Inc.
- 2.
In case you don’t know what GitHub is, you can learn the basics with this guide at https://guides.github.com/activities/hello-world/
- 3.
In deep learning, most of the calculations are done between tensors (multi-dimensional arrays). GPUs and TPUs are chips that are highly optimized to perform such calculations (like matrix multiplications) between very big tensors (up to a million of elements). When developing networks, it is possible to let GPUs and TPUs perform such expensive calculation in Google Colab, speeding up the training of networks.
- 4.
Google Colab documentation is found at https://goo.gl/bKNWy8
- 5.
https://opensource.com/resources/what-docker [Last accessed: 19/12/2018]
- 6.
You can find a list of all compatible GPUs at https://developer.nvidia.com/cuda-gpus and TensorFlow information at https://www.TensorFlow.org/install/gpu .
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© 2019 Umberto Michelucci
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Michelucci, U. (2019). Introduction and Development Environment Setup. In: Advanced Applied Deep Learning . Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-4976-5_1
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DOI: https://doi.org/10.1007/978-1-4842-4976-5_1
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-4975-8
Online ISBN: 978-1-4842-4976-5
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