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TensorFlow 2.x in the Colaboratory Cloud

An Introduction to Deep Learning on Google’s Cloud Service

  • Book
  • © 2021

Overview

Authors:
  1. David Paper

    1. Logan, USA

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  • Introduces Google’s Colab cloud service for executing deep learning applications in Python
  • Provides examples in downloadable Jupyter notebooks for easy execution
  • Teaches foundational principles of deep learning that are needed for success in the field

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xxiii
    Download chapter PDF

  2. Introduction to Deep Learning

    • David Paper
    Pages 1-24

  3. Build Your First Neural Network with Google Colab

    • David Paper
    Pages 25-45

  4. Working with TensorFlow Data

    • David Paper
    Pages 47-65

  5. Working with Other Data

    • David Paper
    Pages 67-99

  6. Classification

    • David Paper
    Pages 101-126

  7. Regression

    • David Paper
    Pages 127-151

  8. Convolutional Neural Networks

    • David Paper
    Pages 153-181

  9. Automated Text Generation

    • David Paper
    Pages 183-202

  10. Sentiment Analysis

    • David Paper
    Pages 203-228

  11. Time Series Forecasting with RNNs

    • David Paper
    Pages 229-256

  12. Back Matter

    Pages 257-264
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Keywords

About this book

Use TensorFlow 2.x with Google's Colaboratory (Colab) product that offers a free cloud service for Python programmers. Colab is especially well suited as a platform for TensorFlow 2.x deep learning applications. You will learn Colab’s default install of the most current TensorFlow 2.x along with Colab’s easy access to on-demand GPU hardware acceleration in the cloud for fast execution of deep learning models. This book offers you the opportunity to grasp deep learning in an applied manner with the only requirement being an Internet connection. Everything else—Python, TensorFlow 2.x, GPU support, and Jupyter Notebooks—is provided and ready to go from Colab. 

The book begins with an introduction to TensorFlow 2.x and the Google Colab cloud service. You will learn how to provision a workspace on Google Colab and build a simple neural network application. From there you will progress into TensorFlow datasets and building input pipelines in support of modeling and testing. You will find coverage of deep learning classification and regression, with clear code examples showing how to perform each of those functions. Advanced topics covered in the book include convolutional neural networks and recurrent neural networks. 



This book contains all the applied math and programming you need to master the content. Examples range from simple to relatively complex when necessary to ensure acquisition of appropriate deep learning concepts and constructs. Examples are carefully explained, concise, accurate, and complete to perfectly complement deep learning skill development. Care is taken to walk you through the foundational principles of deep learning through clear examples written in Python that you can try out and experiment with using Google Colab from the comfort of your own home or office.





What You Will Learn
  • Be familiar with the basic concepts and constructs of applied deep learning
  • Create machine learning models with clean and reliable Python code
  • Work with datasets common to deep learning applications
  • Prepare data for TensorFlow consumption
  • Take advantage of Google Colab’s built-in support for deep learning
  • Execute deep learning experiments using a variety of neural network models
  • Be able to mount Google Colab directly to your Google Drive account
  • Visualize training versus test performance to see model fit




Who This Book Is For


Readers who want to learn the highly popular TensorFlow 2.x deep learning platform, those who wish to master deep learning fundamentals that are sometimes skipped over in the rush to be productive, and those looking to build competency with a modern cloud service tool such as Google Colab



Authors and Affiliations

  • Logan, USA

    David Paper

About the author

Dr. David Paper is a full professor at Utah State University (USU) in the Management Information Systems department. He has over 30 years of higher education teaching experience. At USU, he has over 26 years teaching in the classroom and distance education over satellite. Dr. Paper has taught a variety of classes at the undergraduate, graduate, and doctorate levels, but he specializes in technology education. He has competency in several programming languages, but his focus is currently on deep learning (Python) and database programming (PyMongo). Dr. Paper has published three technical books for industry professionals, including Web Programming for Business: PHP Object-Oriented Programming with Oracle, Data Science Fundamentals for Python and MongoDB (Apress), and Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python (Apress). He has authored more than 100 academic publications. Besides growing up in family businesses, Dr. Paper has worked for Texas Instruments, DLS, Inc., and the Phoenix Small Business Administration. He has performed IS consulting work for IBM, AT&T, Octel, Utah Department of Transportation, and the Space Dynamics Laboratory.

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