Scala: Guide for Data Science Professionals. Build robust data pipelines with Scala

Arun Manivannan, Pascal Bugnion, Patrick R. Nicolas

Scala is especially good for analyzing large sets of data as the scale of the task doesn’t have any significant impact on performance. Scala’s powerful functional libraries can interact with databases and build scalable frameworks — resulting in the creation of robust data pipelines. The first module introduces you to Scala libraries to ingest, store, manipulate, process, and visualize data. Using real world examples, you will learn how to design scalable architecture to process and model data — starting from simple concurrency constructs and progressing to actor systems and Apache Spark. After this, you will also learn how to build interactive visualizations with web frameworks.Once you have become familiar with all the tasks involved in data science, you will explore data analytics with Scala in the second module. You’ll see how Scala can be used to make sense of data through easy to follow recipes. You will learn about Bokeh bindings for exploratory data analysis and quintessential machine learning with algorithms with Spark ML library. You’ll get a sufficient understanding of Spark streaming, machine learning for streaming data, and Spark graphX. Armed with a firm understanding of data analysis, you will be ready to explore the most cutting-edge aspect of data science — machine learning. The final module teaches you the A to Z of machine learning with Scala. You’ll explore Scala for dependency injections and implicits, which are used to write machine learning algorithms. You’ll also explore machine learning topics such as clustering, dimentionality reduction, Naïve Bayes, Regression models, SVMs, neural networks, and more. This learning path combines some of the best that Packt has to offer into one complete, curated package. It includes content from the following Packt products:• Scala for Data Science, Pascal Bugnion• Scala Data Analysis Cookbook, Arun Manivannan • Scala for Machine Learning, Patrick R. Nicolas

Big Data Analytics. Real time analytics using Apache Spark and Hadoop

Venkat Ankam

Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components – HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data.

Ionic: Hybrid Mobile App Development. Create cutting-edge, hybrid mobile applications using the Ionic framework

Rahat Khanna, Sani Yusuf, Hoc Phan

Hybrid Apps are a promising choice in mobile app development to achieve cost-effectiveness and rapid development. Ionic has evolved as the most popular choice for Hybrid Mobile App development as it tends to match the native experience and provides robust components/tools to build apps. The Ionic Complete Developers course takes you on an end–to-end journey, empowering you to build real-time, scalable, and interactive mobile applications with the Ionic framework. Starting with an introduction to the Ionic framework to get you up and running, you will gradually move on to setting up the environment, and work through the multiple options and features available in Ionic to build amazing hybrid mobile apps. You will learn how to use Cordova plugins to include native functionality in your hybrid apps.You will work through three complete projects and build a basic to-do list app, a London tourist app, and a complete social media app. All three projects have been designed to help you learn Ionic at its very best. From setting up your project to developing on both the server side and front end, and best practices for testing and debugging your projects, you'll quickly be able to deliver high-performance mobile apps that look awesome.You will then hone your skills with recipes for cross-platform development. Integrating Ionic with Cordova will bring you native device features, and you will learn about the best modules from its ecosystem. Creating components and customizing the theme will allow you to extend Ionic. You'll see how to build your app to deploy to all platforms to make you a confident start-to-finish mobile developer.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: [*] Getting Started with Ionic – by Rahat Khanna [*] Ionic by Example – by Sani Yusuf [*] Ionic Cookbook – by Hoc Phan

Banana Pi Cookbook. Over 25 recipes to build projects and applications for multiple platforms with Banana Pi

Ryad El-Dajani

React Anti-Patterns. Build efficient and maintainable React applications with test-driven development and refactoring

Juntao Qiu

Take your React development skills to the next level by examining common anti-patterns with expert insights and practical solutions, to refine your codebases into sophisticated and scalable creations. Through this easy-to-follow guide, React Anti-Patterns serves as a roadmap to elevating the efficiency and maintainability of your React projects.You’ll begin by familiarizing yourself with the essential aspects of React before exploring strategies for structuring React applications and creating well-organized, modular, and easy-to-maintain codebases. From identifying and addressing common anti-patterns using refactoring techniques to harnessing the power of test-driven development (TDD), you’ll learn about the tools and techniques necessary to create reliable and robust tests. As you advance, you’ll get to grips with business logic and design patterns that offer solutions to prevalent challenges faced in React development. The book also offers insights into using composition patterns, such as code splitting and multiple entry points, to enhance the flexibility and modularity of your React applications, guiding you through end-to-end project implementation.By the end of this React book, you’ll be able to overcome common challenges and pitfalls to transform your React projects into elegant, efficient, and maintainable codebases.

Hands-On Generative Adversarial Networks with PyTorch 1.x. Implement next-generation neural networks to build powerful GAN models using Python

John Hany, Greg Walters

With continuously evolving research and development, Generative Adversarial Networks (GANs) are the next big thing in the field of deep learning. This book highlights the key improvements in GANs over generative models and guides in making the best out of GANs with the help of hands-on examples.This book starts by taking you through the core concepts necessary to understand how each component of a GAN model works. You'll build your first GAN model to understand how generator and discriminator networks function. As you advance, you'll delve into a range of examples and datasets to build a variety of GAN networks using PyTorch functionalities and services, and become well-versed with architectures, training strategies, and evaluation methods for image generation, translation, and restoration. You'll even learn how to apply GAN models to solve problems in areas such as computer vision, multimedia, 3D models, and natural language processing (NLP). The book covers how to overcome the challenges faced while building generative models from scratch. Finally, you'll also discover how to train your GAN models to generate adversarial examples to attack other CNN and GAN models.By the end of this book, you will have learned how to build, train, and optimize next-generation GAN models and use them to solve a variety of real-world problems.

Interactive Visualization and Plotting with Julia. Create impressive data visualizations through Julia packages such as Plots, Makie, Gadfly, and more

Diego Javier Zea

The Julia programming language offers a fresh perspective into the data visualization field.Interactive Visualization and Plotting with Julia begins by introducing the Julia language and the Plots package. The book then gives a quick overview of the Julia plotting ecosystem to help you choose the best library for your task. In particular, you will discover the many ways to create interactive visualizations with its packages. You’ll also leverage Pluto notebooks to gain interactivity and use them intensively through this book. You’ll find out how to create animations, a handy skill for communication and teaching. Then, the book shows how to solve data analysis problems using DataFrames and various plotting packages based on the grammar of graphics. Furthermore, you’ll discover how to create the most common statistical plots for data exploration. Also, you’ll learn to visualize geographically distributed data, graphs and networks, and biological data. Lastly, this book will go deeper into plot customizations with Plots, Makie, and Gadfly—focusing on the former—teaching you to create plot themes, arrange multiple plots into a single figure, and build new plot types.By the end of this Julia book, you’ll be able to create interactive and publication-quality static plots for data analysis and exploration tasks using Julia.

CoffeeScript Application Development Cookbook. Over 90 hands-on recipes to help you develop engaging applications using CoffeeScript

Mike Hatfield