As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to basic deep learning concepts and incorporate that knowledge into Angular 10 applications. It is intended to be a fast-paced introduction to some basic features of deep learning and an overview of several popular deep learning classifiers. The book includes code samples and numerous figures and covers topics such as Angular 10 functionality, basic deep learning concepts, classification algorithms, TensorFlow, and Keras. Companion files with code and color figures are included. FEATURES: Introduces basic deep learning concepts and Angular 10 applications Covers MLPs (MultiLayer Perceptrons) and CNNs (Convolutional Neural Networks), RNNs (Recurrent Neural Networks), LSTMs (Long Short-Term Memory), GRUs (Gated Recurrent Units), autoencoders, and GANs (Generative Adversarial Networks) Introduces TensorFlow 2 and Keras Includes companion files with source code and 4-color figures. The companion files are also available online by emailing the publisher with proof of purchase at [email protected].

This book is for developers who are looking for an overview of basic concepts in Natural Language Processing using R. It casts a wide net of techniques to help developers who have a range of technical backgrounds. Numerous code samples and listings are included to support myriad topics. The final chapter presents the Transformer Architecture, BERT-based models, and the GPT family of models, all of which were developed during the past three years. Companion files with source code and figures are included and available for downloading by emailing the publisher at [email protected] with proof of purchase. FEATURES: Covers extensive topics related to natural language processing using R Features companion files with source code and figures from the book

As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to basic machine learning concepts and incorporate that knowledge into Angular applications. The book is intended to be a fast-paced introduction to some basic features of machine learning and an overview of several popular machine learning classifiers. It includes code samples and numerous figures and covers topics such as Angular functionality, basic machine learning concepts, classification algorithms, TensorFlow and Keras. The files with code and color figures are on the companion disc with the book or available from the publisher. Features: Introduces the basic machine learning concepts and Angular applications Includes source code and full color figures

The goal of this book is to introduce readers to an assortment of powerful command line utilities that can be combined to create simple, yet powerful shell scripts. While all examples and scripts use the “bash” command set, many of the concepts translate into other forms of shell scripting (ksh, sh, csh), including the concept of piping data between commands, regular expression substitution and the sed and awk commands. Aimed at a reader relatively new to working in a bash environment, the book is comprehensive enough to be a good reference and teach a few new tricks to those who already have some experience with creating shell scripts. FEATURES: Covers extensive topics, code samples, and scripting utilities Includes material on piping data between commands, regular expression substitution, cleaning datasets, and the sed and awk commands Features companion files with code samples from the book (available for downloading from the publisher)

As part of the best-selling Pocket Primer series, this book is designed to introduce readers to an assortment of useful command-line utilities that can be combined to create simple, yet powerful shell scripts. While all examples and scripts use the “bash” command set, many of the concepts translate into other command shells (such as sh, ksh, zsh, and csh), including the concept of piping data between commands and the highly versatile sed and awk commands. Aimed at a reader relatively new to working in a bash environment, the book is comprehensive enough to be a good reference and teach a few new techniques to those who already have some experience with creating shell scripts. It contains a variety of code fragments and shell scripts for data scientists, data analysts, and other people who want shell-based solutions to “clean” various types of text files. In addition, the concepts and code samples in this book are useful for people who want to simplify routine tasks. Includes companion files with all of the source code examples (download from the publisher by writing to [email protected]). Features: Takes introductory concepts and commands in bash, and then demonstrates their uses in simple, yet powerful shell scripts Contains an assortment of shell scripts for data scientists, data analysts, and other people who want shell-based solutions to “clean” various types of text files Includes companion files with all of the source code examples (available for download from the publisher)

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 [email protected]. 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)