Python for TensorFlow Pocket Primer

Python for TensorFlow Pocket Primer

by Oswald Campesato


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As part of the best-selling Pocket
series, this book is designed to prepare programmers for machine learning and deep learning/TensorFlow topics. It begins with a quick introduction to Python, followed by chapters that discuss NumPy, Pandas,
Matplotlib, and scikit-learn. The final two chapters contain an assortment of
TensorFlow 1.x code samples, including detailed code samples for TensorFlow
Dataset (which is used heavily in TensorFlow 2 as well). A TensorFlow Dataset refers to the classes in the namespace that enables programmers to construct a pipeline of data by means of method chaining so-called lazy operators, e.g., map(), filter(), batch(), and so forth, based on data from one or more data sources.

Companion files with source code are
available for downloading from the publisher by writing


  • A practical introduction to Python, NumPy, Pandas, Matplotlib, and introductory aspects of TensorFlow
  • Contains relevant NumPy/Pandas code samples that are typical in machine learning topics, and also useful
    TensorFlow 1.x code samples for deep learning/TensorFlow topics
  • Includes many examples of TensorFlow Dataset APIs with lazy operators, e.g., map(), filter(), batch(), take() and also method chaining such operators
  • Assumes the reader has very limited experience
  • Companion files with all of the source code examples (download from the publisher)

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Product Details

ISBN-13: 9781683923619
Publisher: Mercury Learning and Information
Publication date: 06/03/2019
Series: Pocket Primer
Pages: 218
Sales rank: 1,144,708
Product dimensions: 6.00(w) x 8.90(h) x 0.60(d)

About the Author

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

Table of Contents

Introduction to Python

2: NumPy

3: Pandas

4: Matplotlib, Sklearn, and

Introduction to TensorFlow

6: TensorFlow Datasets


(available from the publisher for downloading by writing

  • Source code samples
  • Figures

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