Hands-On Server-Side Web Development with Swift. Build dynamic web apps by leveraging two popular Swift web frameworks: Vapor 3.0 and Kitura 2.5

Angus Yeung

This book is about building professional web applications and web services using Swift 4.0 and leveraging two popular Swift web frameworks: Vapor 3.0 and Kitura 2.5. In the first part of this book, we’ll focus on the creation of basic web applications from Vapor and Kitura boilerplate projects. As the web apps start out simple, more useful techniques, such as unit test development, debugging, logging, and the build and release process, will be introduced to readers.In the second part, we’ll learn different aspects of web application development with server-side Swift, including setting up routes and controllers to process custom client requests, working with template engines such as Leaf and Stencil to create dynamic web content, beautifying the content with Bootstrap, managing user access with authentication framework, and leveraging the Object Relational Mapping (ORM) abstraction layer (Vapor’s Fluent and Kitura’s Kuery) to perform database operations.Finally, in the third part, we’ll develop web services in Swift and build our API Gateway, microservices and database backend in a three-tier architecture design. Readers will learn how to design RESTful APIs, work with asynchronous processes, and leverage container technology such as Docker in deploying microservices to cloud hosting services such as Vapor Cloud and IBM Cloud.

Machine Learning Algorithms. Popular algorithms for data science and machine learning - Second Edition

Giuseppe Bonaccorso

Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight.This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture.By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.

Hands-On Deep Learning with TensorFlow. Uncover what is underneath your data!

Dan Van Boxel

Dan Van Boxel’s Deep Learning with TensorFlow is based on Dan’s best-selling TensorFlow video course. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Dan Van Boxel will be your guide to exploring the possibilities with deep learning; he will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data.With Dan’s guidance, you will dig deeper into the hidden layers of abstraction using raw data. Dan then shows you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. In this book, Dan shares his knowledge across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces. With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond.

GitLab Quick Start Guide. Migrate to GitLab for all your repository management solutions

Adam O'Grady

Gitlab is an open source repository management and version control toolkit with an enterprise offering. This book is the ideal guide to GitLab as a version control system (VCS), issue management tool, and a continuous integration platform.The book starts with an introduction to GitLab, a walkthrough of its features, and explores concepts such as version control systems, continuous integration, and continuous deployment. It then takes you through the process of downloading and installing a local copy of the on-premise version of GitLab in Ubuntu and/or CentOS.You will look at some common work?ows associated with GitLab work?ow and learn about project management in GitLab. You will see tools and techniques for migrating your code base from various version control systems such as GitHub and SVN to GitLab.By the end of the book, you will be using Gitlab for repository management, and be able to migrate projects from other VCSs to GitLab.

Distributed Computing in Java 9. Leverage the latest features of Java 9 for distributed computing

Raja Malleswara Rao Malleswara Rao Pattamsetti

Distributed computing is the concept with which a bigger computation process is accomplished by splitting it into multiple smaller logical activities and performed by diverse systems, resulting in maximized performance in lower infrastructure investment. This book will teach you how to improve the performance of traditional applications through the usage of parallelism and optimized resource utilization in Java 9.After a brief introduction to the fundamentals of distributed and parallel computing, the book moves on to explain different ways of communicating with remote systems/objects in a distributed architecture. You will learn about asynchronous messaging with enterprise integration and related patterns, and how to handle large amount of data using HPC and implement distributed computing for databases. Moving on, it explains how to deploy distributed applications on different cloud platforms and self-contained application development. You will also learn about big data technologies and understand how they contribute to distributed computing. The book concludes with the detailed coverage of testing, debugging, troubleshooting, and security aspects of distributed applications so the programs you build are robust, efficient, and secure.

Mastering Machine Learning with R. Advanced machine learning techniques for building smart applications with R 3.5 - Third Edition

Cory Lesmeister

Given the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models. This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You’ll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you’ll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You’ll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you’ll get a glimpse into how some of these blackbox models can be diagnosed and understood. By the end of this book, you’ll be equipped with the skills to deploy ML techniques in your own projects or at work.

Deep Learning By Example. A hands-on guide to implementing advanced machine learning algorithms and neural networks

Ahmed Menshawy

Deep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic.This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book.By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence.

Nmap: Network Exploration and Security Auditing Cookbook. Network discovery and security scanning at your fingertips - Second Edition

Paulino Calderon

This is the second edition of ‘Nmap 6: Network Exploration and Security Auditing Cookbook’. A book aimed for anyone who wants to master Nmap and its scripting engine through practical tasks for system administrators and penetration testers. Besides introducing the most powerful features of Nmap and related tools, common security auditing tasks for local and remote networks, web applications, databases, mail servers, Microsoft Windows machines and even ICS SCADA systems are explained step by step with exact commands and argument explanations. The book starts with the basic usage of Nmap and related tools like Ncat, Ncrack, Ndiff and Zenmap. The Nmap Scripting Engine is thoroughly covered through security checks used commonly in real-life scenarios applied for different types of systems. New chapters for Microsoft Windows and ICS SCADA systems were added and every recipe was revised. This edition reflects the latest updates and hottest additions to the Nmap project to date. The book will also introduce you to Lua programming and NSE script development allowing you to extend further the power of Nmap.