Python for Tensor Flow Pocket Primer

by Oswald Campesato

Overview

As part of the best-selling Pocket Primer 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 tf.data.Dataset 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 [e-mail].

Features:

• A practical introduction to Python, NumPy, Pandas, Matplotlib, and introductory aspects of TensorFlow 1.x
• 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)

Product Details

ISBN-13:	9781683923626
Publisher:	Mercury Learning & Information
Publication date:	05/09/2019
Series:	Pocket Primer
Sold by:	Barnes & Noble
Format:	eBook
Pages:	200
File size:	4 MB

About the Author

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

Table of Contents

1: Introduction to Python

2: NumPy

3: Pandas

4: Matplotlib, Sklearn, and Seaborn

5: Introduction to TensorFlow

6: TensorFlow Datasets

ON THE COMPANION FILES!

(available from the publisher for downloading by writing [e-mail])

• Source code samples
• Figures