Table of Contents

1. Build TensorFlow Input Pipelines.- 2. Increase the Diversity of your Dataset with Data Augmentation.- 3. TensorFlow Datasets.- 4. Deep Learning with TensorFlow Datasets.- 5. Introduction to Tensor Processing Units.- 6. Simple Transfer Learning with TensorFlow Hub.- 7. Advanced Transfer Learning.- 8. Stacked Autoencoders.- 9. Convolutional and Variational Autoencoders.- 10. Generative Adversarial Networks.- 11. Progressive Growing Generative Adversarial Networks.- 12. Fast Style Transfer.- 13. Object Detection.- 14. An Introduction to Reinforcement Learning.

About the Author

​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.