The French Revolution and Napoleon

The Garden of Eden

Charles the Bold

Cleopatra

Europe in the Middle Ages

King Coal

The Land that Time Forgot

Dead Man's Love

Madame Bovary

Dr. Heidenhoff's Process

Moby Dick

Tarzan the Terrible

Dracula's Guest

What Men Live By and Other Tales

Henry VIII and His Court

History of Europe 1500-1815

My Strangest Case

Discover how to use the next-generation language Red for full-stack development, from systems coding over user-interfaces to blockchain programming About This Book ? Explore the latest features of Red to build scalable, fast, and secure applications ? Learn graphical programming and build highly sophisticated reactive applications ? Get familiar with the specific concepts and techniques of Red development, like working with series, viewing code as data, and using dialects. Who This Book Is For This book is for software developers and architects who want to learn Red because of its conciseness, flexibility, and expressiveness, and more specifically for its possibilities in GUI apps and blockchain / smart contracts programming. Some knowledge of the basic concepts and experience of any programming language is assumed. What You Will Learn ? Set up your Red environment to achieve the highest productivity ? Get grounded in Red, gaining experience and insight through many examples and exercises ? Build simple, compact, and portable applications ? Analyze streams of data through Parse ? Compose GUI applications with View and Draw ? Get prepared for smart contract blockchain programming in Red In Detail A key problem of software development today is software bloat, where huge toolchains and development environments are needed in software coding and deployment. Red significantly reduces this bloat by offering a minimalist but complete toolchain. This is the first introductory book about it, and it will get you up and running with Red as quickly as possible. This book shows you how to write effective functions, reduce code redundancies, and improve code reuse. It will be helpful for new programmers who are starting out with Red to explore its wide and ever-growing package ecosystem and also for experienced developers who want to add Red to their skill set. The book presents the fundamentals of programming in Red and in-depth informative examples using a step-by-step approach. You will be taken through concepts and examples such as doing simple metaprogramming, functions, collections, GUI applications, and more. By the end of the book, you will be fully equipped to start your own projects in Red. Style and approach This book will gently guide you step by step into the fascinating programming universe of the Red language, offering real-world examples and practical exercises to sharpen your insight.

Build next-generation Artificial Intelligence systems with Java About This Book ? Implement AI techniques to build smart applications using Deeplearning4j ? Perform big data analytics to derive quality insights using Spark MLlib ? Create self-learning systems using neural networks, NLP, and reinforcement learning Who This Book Is For This book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus. What You Will Learn ? Manage Artificial Intelligence techniques for big data with Java ? Build smart systems to analyze data for enhanced customer experience ? Learn to use Artificial Intelligence frameworks for big data ? Understand complex problems with algorithms and Neuro-Fuzzy systems ? Design stratagems to leverage data using Machine Learning process ? Apply Deep Learning techniques to prepare data for modeling ? Construct models that learn from data using open source tools ? Analyze big data problems using scalable Machine Learning algorithms In Detail In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems. Style and approach An easy-to-follow, step-by-step guide to help you get to grips with real-world applications of Artificial Intelligence for big data using Java

Explore the features included in the latest versions of MQTT for IoT and M2M communications and use them with modern Python 3. About This Book ? Make your connected devices less prone to attackers by understanding security mechanisms ? Take advantage of MQTT features for IoT and Machine-to-Machine communications ? The only book that covers MQTT with a single language, Python Who This Book Is For This book is for developers who want to learn about the MQTT protocol for their IoT projects. Prior knowledge of working with IoT and Python will be helpful. What You Will Learn ? Learn how MQTT and its lightweight messaging system work ? Understand the MQTT puzzle: clients, servers (formerly known as brokers), and connections ? Explore the features included in the latest versions of MQTT for IoT and M2M communications ? Publish and receive MQTT messages with Python ? Learn the difference between blocking and threaded network loops ? Take advantage of the last will and testament feature ? Work with cloud-based MQTT interfaces in Python In Detail MQTT is a lightweight messaging protocol for small sensors and mobile devices. This book explores the features of the latest versions of MQTT for IoT and M2M communications, how to use them with Python 3, and allow you to interact with sensors and actuators using Python. The book begins with the specific vocabulary of MQTT and its working modes, followed by installing a Mosquitto MQTT broker. You will use different utilities and diagrams to understand the most important concepts related to MQTT. You will learn to?make all the necessary configuration to work with digital certificates for encrypting all data sent between the MQTT clients and the server. You will also work with the different Quality of Service levels and later analyze and compare their overheads. You will write Python 3.x code to control a vehicle with MQTT messages delivered through encrypted connections (TLS 1.2), and learn how leverage your knowledge of the MQTT protocol to build a solution based on requirements. Towards the end, you will write Python code to use the PubNub cloud-based real-time MQTT provider to monitor a surfing competition. In the end, you will have a solution that was built from scratch by analyzing the requirements and then write Python code that will run on water-proof IoT boards connected to multiple sensors in surfboards. Style and approach This book shows you what MQTT is, and how to install and secure an MQTT server. You will write Python 3 code to control a vehicle with MQTT messages, test and improve, then monitor a surfing competition with cloud-based real-time MQTT providers.

A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript About This Book ? Solve complex computational problems in browser with JavaScript ? Teach your browser how to learn from rules using the power of machine learning ? Understand discoveries on web interface and API in machine learning Who This Book Is For This book is for you if you are a JavaScript developer who wants to implement machine learning to make applications smarter, gain insightful information from the data, and enter the field of machine learning without switching to another language. Working knowledge of JavaScript language is expected to get the most out of the book. What You Will Learn ? Get an overview of state-of-the-art machine learning ? Understand the pre-processing of data handling, cleaning, and preparation ? Learn Mining and Pattern Extraction with JavaScript ? Build your own model for classification, clustering, and prediction ? Identify the most appropriate model for each type of problem ? Apply machine learning techniques to real-world applications ? Learn how JavaScript can be a powerful language for machine learning In Detail In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added advantage of machine learning research and support for JS libraries, JavaScript makes your browsers smarter than ever with the ability to learn patterns and reproduce them to become a part of innovative products and applications. Hands-on Machine Learning with JavaScript presents various avenues of machine learning in a practical and objective way, and helps implement them using the JavaScript language. Predicting behaviors, analyzing feelings, grouping data, and building neural models are some of the skills you will build from this book. You will learn how to train your machine learning models and work with different kinds of data. During this journey, you will come across use cases such as face detection, spam filtering, recommendation systems, character recognition, and more. Moreover, you will learn how to work with deep neural networks and guide your applications to gain insights from data. By the end of this book, you'll have gained hands-on knowledge on evaluating and implementing the right model, along with choosing from different JS libraries, such as NaturalNode, brain, harthur, classifier, and many more to design smarter applications. Style and approach This is a practical tutorial that uses hands-on examples to step through some real-world applications of machine learning. Without shying away from the technical details, you will explore machine learning with JavaScript using clear and practical examples.