State-of-the-Art Deep Learning Models in TensorFlow

1. Front Matter

   Pages i-xxiv

   PDF

2. Build TensorFlow Input Pipelines

   • David Paper

   Pages 1-36

3. Increase the Diversity of Your Dataset with Data Augmentation

   • David Paper

   Pages 37-64

4. TensorFlow Datasets

   • David Paper

   Pages 65-91

5. Deep Learning with TensorFlow Datasets

   • David Paper

   Pages 93-125

6. Introduction to Tensor Processing Units

   • David Paper

   Pages 127-152

7. Simple Transfer Learning with TensorFlow Hub

   • David Paper

   Pages 153-169

8. Advanced Transfer Learning

   • David Paper

   Pages 171-199

9. Stacked Autoencoders

   • David Paper

   Pages 201-217

10. Convolutional and Variational Autoencoders

    • David Paper

    Pages 219-241

11. Generative Adversarial Networks

    • David Paper

    Pages 243-263

12. Progressive Growing Generative Adversarial Networks

    • David Paper

    Pages 265-294

13. Fast Style Transfer

    • David Paper

    Pages 295-319

14. Object Detection

    • David Paper

    Pages 321-339

15. An Introduction to Reinforcement Learning

    • David Paper

    Pages 341-364

16. Back Matter

    Pages 365-374

    PDF

Use TensorFlow 2.x in the Google Colab ecosystem to create state-of-the-art deep learning models guided by hands-on examples. The Colab ecosystem provides a free cloud service with easy access to on-demand GPU (and TPU) hardware acceleration for fast execution of the models you learn to build. This book teaches you state-of-the-art deep learning models in an applied manner with the only requirement being an Internet connection. The Colab ecosystem provides everything else that you need, including Python, TensorFlow 2.x, GPU and TPU support, and Jupyter Notebooks.

The book begins with an example-driven approach to building input pipelines that feed all machine learning models. You will learn how to provision a workspace on the Colab ecosystem to enable construction of effective input pipelines in a step-by-step manner. From there, you will progress into data augmentation techniques and TensorFlow datasets to gain a deeper understanding of how to work with complex datasets. You will find coverage of Tensor Processing Units (TPUs) and transfer learning followed by state-of-the-art deep learning models, including autoencoders, generative adversarial networks, fast style transfer, object detection, and reinforcement learning.

Author Dr. Paper provides all the applied math, programming, and concepts you need to master the content. Examples range from relatively simple to very complex when necessary. Examples are carefully explained, concise, accurate, and complete. Care is taken to walk you through each topic through clear examples written in Python that you can try out and experiment with in the Google Colab ecosystem in the comfort of your own home or office.

What You Will Learn

• Take advantage of the built-in support of the Google Colab ecosystem
• Work with TensorFlow data sets
  
• Create input pipelines to feed state-of-the-art deep learning models
  
• Create pipelined state-of-the-art deep learning models with clean and reliable Python code
  
• Leverage pre-trained deep learning models to solve complex machine learning tasks
  
• Create a simple environment to teach an intelligent agent to make automated decisions
  

Who This Book Is For


Readers who want to learn the highly popular TensorFlow deep learning platform, those who wish to master the basics of state-of-the-art deep learning models, and those looking to build competency with a modern cloud service tool such as Google Colab

• Google Colab
• Colaboratory Cloud
• TensorFlow 2.x
• Deep Learning Models
• Tensors
• tf.data API
• tf.data Datasets
• Data Augmentation
• Tensor Processing Unit (TPU)
• Hands-on Transfer Learning
• TensorFlow Datasets
• Autoencoders
• Generative Adversarial Networks (GENs)
• Object Detection
• Image Detection
• Neural Style Transfer
• Reinforcement Learning
• Python Machine Learning
• Deep Learning with TensorFlow
• Fast Style Transfer

• Logan, USA

  David Paper

​Dr. Paper is a retired academic from the Utah State University (USU) Data Analytics and Management Information Systems department in the Huntsman School of Business. He has over 30 years of higher education teaching experience. At USU, he taught for 27 years in the classroom and distance education over satellite. He taught a variety of classes at the undergraduate, graduate, and doctorate levels, but he specializes in applied technology education.

Dr. Paper has competency in several programming languages, but his focus is currently on deep learning with Python in the TensorFlow-Colab Ecosystem. He has published extensively on machine learning, including Apress books: Data Science Fundamentals for Python and MongoDB, Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python, and TensorFlow 2.x in the Colaboratory Cloud: An Introduction to Deep Learning on Google’s Cloud Service. He has also published more than 100 academic articles.


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, the Utah Department of Transportation, and the Space Dynamics Laboratory. He has worked on research projects with several corporations, including Caterpillar, Fannie Mae, Comdisco, IBM, RayChem, Ralston Purina, and Monsanto. He maintains contacts in corporations such as Google, Micron, Oracle, and Goldman Sachs.

 

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