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Vinita Silaparasetty - Deep Learning Projects Using TensorFlow 2: Neural Network Development with Python and Keras

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Vinita Silaparasetty Deep Learning Projects Using TensorFlow 2: Neural Network Development with Python and Keras
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Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will lead new programmers through the basics into developing practical deep learning applications.
Deep learning is quickly integrating itself into the technology landscape. Its applications range from applicable data science to deep fakes and so much more. It is crucial for aspiring data scientists or those who want to enter the field of AI to understand deep learning concepts.
The best way to learn is by doing. Youll develop a working knowledge of not only TensorFlow, but also related technologies such as Python and Keras. Youll also work with Neural Networks and other deep learning concepts. By the end of the book, youll have a collection of unique projects that you can add to your GitHub profiles and expand on for professional application.
What Youll Learn
  • Grasp the basic process of neural networks through projects, such as creating music
  • Restore and colorize black and white images with deep learning processes
Who This Book Is For
Beginners new to TensorFlow and Python.

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Vinita Silaparasetty - Deep Learning Projects Using TensorFlow 2: Neural Network Development with Python and Keras
Deep Learning Projects Using TensorFlow 2: Neural Network Development with Python and Keras
Vinita Silaparasetty
Deep Learning Projects Using TensorFlow 2: Neural Network Development with Python and Keras

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Vinita Silaparasetty Deep Learning Projects Using TensorFlow 2 Neural Network - photo 1
Vinita Silaparasetty
Deep Learning Projects Using TensorFlow 2
Neural Network Development with Python and Keras
1st ed.
Vinita Silaparasetty Bangalore India Any source code or other supplementary - photo 2
Vinita Silaparasetty
Bangalore, India

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-5801-9 . For more detailed information, please visit http://www.apress.com/source-code .

ISBN 978-1-4842-5801-9 e-ISBN 978-1-4842-5802-6
https://doi.org/10.1007/978-1-4842-5802-6
Apress Standard
Vinita Silaparasetty 2020
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Distributed to the book trade worldwide by Springer Science+Business Media New York, 233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax (201) 348-4505, e-mail orders-ny@springer-sbm.com, or visit www.springeronline.com. Apress Media, LLC is a California LLC and the sole member (owner) is Springer Science + Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation.
Preface

TensorFlow 2.0 was officially released on September 30th, 2019. However, the new version is very different than what most users are familiar with. While programming with TensorFlow 2.0 is much simpler, most users still prefer to use older versions. This book aims to help long-time users of TensorFlow adjust to TensorFlow 2.0 and to help absolute beginners learn TensorFlow 2.0.

Why use TensorFlow?
Here are some advantages to using TensorFlow for your deep learning projects.

  • It is open source.

  • It is reliable (has minimal major bugs).

  • It is ideal for perceptual and language understanding tasks.

  • It is capable of running on CPUs and GPUs.

  • It is easier to debug.

  • It uses graphs for numeric computations.

  • It has better scalability, as libraries can be deployed on a gamut of hardware machines, starting from cellular devices to computers with complex setups.

  • It has convenient pipelining, as it is highly parallel and designed to use various backend software (GPU, ASIC, etc.).

  • It uses the high-level Keras API.

  • It has better compatibility.

  • It uses TensorFlow Extended (TFX) for a full production ML pipeline.

  • It also supports an ecosystem of powerful add-on libraries and models to experiment with, including Ragged Tensors, TensorFlow Probability, Tensor2Tensor, and BERT.

Figure I-1 Comparison of TensorFlow 1x and TensorFlow 20 About the Book - photo 3
Figure I-1

Comparison of TensorFlow 1.x and TensorFlow 2.0

About the Book Projects

The projects in this book mainly cover image and sound data. They are designed to be as simple as possible to help you understand how each neural network works. Consider them to be a skeletal structure for your own projects. You are encouraged to build on the models in this book and experiment with them using different datasets. The projects in this book were designed keeping in mind the latest developments in deep learning and will be the perfect addition for an impressive data science portfolio.

System Specifications
The projects in this book require powerful computing resources or a good cloud platform. You are strongly advised to use a system with the following minimum requirements :

  • GPU: Model: 16-bit Memory: 8GB and CUDA Toolkit support

  • RAM: Memory: 10GB

  • CPU: PCIe lanes: 8 Core: 4 threads per GPU

  • SSD: Form Factor: 2.5-inch and SATA interface

  • PSU: 16.8 watts

  • Motherboard: PCIe lanes: 8

If you are unable to acquire a system with these requirements, try using a cloud computing platform, such as one of the following:

  • BigML

  • Amazon Web Services

  • Microsoft Azure

  • Google Cloud

  • Alibaba Cloud

  • Kubernetes

Tips to Get the Most Out of This Book
To get the most value out of the projects in this book, follow these guidelines:

  • Create separate environments. To prevent problems, its best to create separate environments for each project. This way you will have only the libraries necessary for that particular project and there will not be any clashes.

  • Save your projects in separate folders. To keep your work organized and handy for future reference, create separate folders for each project. You can store the script, datasets, and results that you have obtained in that folder. Each project in this book provides the code to set your file path to work directly in the project folder that you created.

  • Use data wisely. Ensure that you have enough data to divide into training and test sets. I suggest that you use 80% of the data for training and 20% for testing.

  • Be organized. By creating a folder for your project, you know that all the data, output files, etc. are available in one place.

  • Make backups. Make copies of each notebook before experimenting. This way you have one working copy as a template for future projects. Then make copies of it and modify it as required.

  • Plan. Understand the problem statement and create a rough flowchart of your approach to solving the problem.

  • Consider your presentation. As a data scientist, your inferences will be discussed by members of a company who have technical knowledge as well as those who do not. So be sure that you can convey your findings in a manner that anyone can understand.

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