Series:

Computer Science

5 total works

Cover of Regular Expressions

Regular Expressions

by Oswald Campesato

Published 4 June 2018
No detailed description available for "Regular Expressions".

Cover of Data Cleaning Pocket Primer

Data Cleaning Pocket Primer

by Oswald Campesato

Published 7 February 2018
As part of the best-selling Pocket Primer series, this book is an effort to give programmers sufficient knowledge of data cleaning to be able to work on their own projects. It is designed as a practical introduction to using flexible, powerful (and free) Unix/Linux shell commands to perform common data cleaning tasks. The book is packed with realistic examples and numerous commands that illustrate both the syntax and how the commands work together. Companion files with source code are available for downloading from the publisher. FEATURES: A practical introduction to using flexible, powerful (and free) Unix/Linux shell commands to perform common data cleaning tasks. Includes the concept of piping data between commands, regular expression substitution, and the 'sed' and 'awk' commands. Packed with realistic examples and numerous commands that illustrate both the syntax and how the commands work together. Assumes the reader has no prior experience, but the topic is covered comprehensively enough to teach a pro some new tricks.

Cover of Data Science Fundamentals Pocket Primer
No detailed description available for "Data Science Fundamentals Pocket Primer".

Cover of Data Wrangling Using Pandas, SQL, and Java
No detailed description available for "Data Wrangling Using Pandas, SQL, and Java".

Cover of TensorFlow 2 Pocket Primer

As part of the best-selling Pocket Primer series, this book is designed to introduce beginners to basic machine learning algorithms using TensorFlow 2. It is intended to be a fast-paced introduction to various “core” features of TensorFlow, with code samples that cover machine learning and TensorFlow basics. A comprehensive appendix contains some Keras-based code samples and the underpinnings of MLPs, CNNs, RNNs, and LSTMs. 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 emailing proof of purchase to info@merclearning.com.

Features:

  • Uses Python for code samples
  • Covers TensorFlow 2 APIs and Datasets
  • Includes a comprehensive appendix that covers Keras and advanced topics such as NLPs, MLPs, RNNs, LSTMs
  • Features the companion files with all of the source code examples and figures (download from the publisher)