TensorFlow Pocket Primer

As part of the best-selling Pocket Primer series, this book is designed to introduce beginners to TensorFlow 1.x fundamentals for basic machine learning algorithms in TensorFlow. It is intended to be a fast-paced introduction to various “core” features of TensorFlow, with code samples that cover deep learning and TensorFlow basics. The material in the chapters illustrates how to solve a variety of tasks after which you can do further reading to deepen your knowledge. Companion files with all of the code samples are available for downloading from the publisher by writing to info@merclearning.com.

Features:

• Uses Python for code samples
• Covers TensorFlow APIs and Datasets
• Assumes the reader has very limited experience
• Companion files with all of the source code examples (download from the publisher)

Oswald Campesato (San Francisco, CA) is an adjunct instructor at UC-Santa Clara and specializes in Deep Learning, Java, Android, TensorFlow, and NLP. He is the author/co-author of over twenty-five books including TensorFlow 2 Pocket Primer, Python 3 for Machine Learning, and the NLP Using R Pocket Primer (all Mercury Learning and Information).

"TensorFlow Pocket Primer introduces readers to TensorFlow 1x basics for machine learning algorithms, and is designed to be an introduction used either to supplement a course or for self-learning. It uses Python to cover code examples, assumes limited experience and background in the subject, and comes with supporting reference files containing all source code examples as a download from the publisher. From Cloud-based platforms to useful components of TensorFlow and their real-world applications, this primer will get anyone up and running in the shortest amount of time possible."