R Machine Learning Projects. Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5

Dr. Sunil Kumar Chinnamgari

R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization.This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine.By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.

Java: High-Performance Apps with Java 9. Boost your application’s performance with the new features of Java 9

Mayur Ramgir

Java 9 which is one of the most popular application development languages. The latest released version Java 9 comes with a host of new features and new APIs with lots of ready to use components to build efficient and scalable applications. Streams, parallel and asynchronous processing, multithreading, JSON support, reactive programming, and microservices comprise the hallmark of modern programming and are now fully integrated into the JDK. This book focuses on providing quick, practical solutions to enhance your application's performance. You will explore the new features, APIs, and various tools added in Java 9 that help to speed up the development process. You will learn about jshell, Ahead-of-Time (AOT) compilation, and the basic threads related topics including sizing and synchronization. You will also explore various strategies for building microservices including container-less, self-contained, and in-container. This book is ideal for developers who would like to build reliable and high-performance applications with Java. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. This book is repurposed for this specific learning experience from material from Packt's Java 9 High Performance by Mayur Ramgir and Nick Samoylov

Mastering Assembly Programming. From instruction set to kernel module with Intel processor

Alexey Lyashko

The Assembly language is the lowest level human readable programming language on any platform. Knowing the way things are on the Assembly level will help developers design their code in a much more elegant and efficient way. It may be produced by compiling source code from a high-level programming language (such as C/C++) but can also be written from scratch. Assembly code can be converted to machine code using an assembler.The first section of the book starts with setting up the development environment on Windows and Linux, mentioning most common toolchains. The reader is led through the basic structure of CPU and memory, and is presented the most important Assembly instructions through examples for both Windows and Linux, 32 and 64 bits. Then the reader would understand how high level languages are translated into Assembly and then compiled into object code. Finally we will cover patching existing code, either legacy code without sources or a running code in same or remote process.

Machine Learning for Healthcare Analytics Projects. Build smart AI applications using neural network methodologies across the healthcare vertical market

Eduonix Learning Solutions

Machine Learning (ML) has changed the way organizations and individuals use data to improve the efficiency of a system. ML algorithms allow strategists to deal with a variety of structured, unstructured, and semi-structured data. Machine Learning for Healthcare Analytics Projects is packed with new approaches and methodologies for creating powerful solutions for healthcare analytics.This book will teach you how to implement key machine learning algorithms and walk you through their use cases by employing a range of libraries from the Python ecosystem. You will build five end-to-end projects to evaluate the efficiency of Artificial Intelligence (AI) applications for carrying out simple-to-complex healthcare analytics tasks. With each project, you will gain new insights, which will then help you handle healthcare data efficiently. As you make your way through the book, you will use ML to detect cancer in a set of patients using support vector machines (SVMs) and k-Nearest neighbors (KNN) models. In the final chapters, you will create a deep neural network in Keras to predict the onset of diabetes in a huge dataset of patients. You will also learn how to predict heart diseases using neural networks.By the end of this book, you will have learned how to address long-standing challenges, provide specialized solutions for how to deal with them, and carry out a range of cognitive tasks in the healthcare domain.

Building Computer Vision Projects with OpenCV 4 and C++. Implement complex computer vision algorithms and explore deep learning and face detection

David Millán Escrivá, Prateek Joshi, Vinícius G. Mendonça, Roy Shilkrot

OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation.This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books:•Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millán Escrivá•Learn OpenCV 4 By Building Projects - Second Edition by David Millán Escrivá, Vinícius G. Mendonça, and Prateek Joshi

Hands-On Microservices - Monitoring and Testing. A performance engineer's guide to the continuous testing and monitoring of microservices

Dinesh Rajput

Microservices are the latest right way of developing web applications. Microservices architecture has been gaining momentum over the past few years, but once you've started down the microservices path, you need to test and optimize the services. This book focuses on exploring various testing, monitoring, and optimization techniques for microservices.The book starts with the evolution of software architecture style, from monolithic to virtualized, to microservices architecture. Then you will explore methods to deploy microservices and various implementation patterns. With the help of a real-world example, you will understand how external APIs help product developers to focus on core competencies. After that, you will learn testing techniques, such as Unit Testing, Integration Testing, Functional Testing, and Load Testing. Next, you will explore performance testing tools, such as JMeter, and Gatling. Then, we deep dive into monitoring techniques and learn performance benchmarking of the various architectural components. For this, you will explore monitoring tools such as Appdynamics, Dynatrace, AWS CloudWatch, and Nagios. Finally, you will learn to identify, address, and report various performance issues related to microservices.

BeagleBone Robotic Projects. Build and control robots that walk, swim, roll, and fly - Second Edition

Richard Grimmett

BeagleBone Blue is effectively a small, light, cheap computer in a similar vein to Raspberry Pi and Arduino. It has all of the extensibility of today’s desktop machines, but without the bulk, expense, or noise. This project guide provides step-by-step instructions that enable anyone to use this new, low-cost platform in some fascinating robotics projects. By the time you are finished, your projects will be able to see, speak, listen,detect their surroundings, and move in a variety of amazing ways.The book begins with unpacking and powering up the components. This includes guidance on what to purchase and how to connect it all successfully, and a primer on programming the BeagleBone Blue. You will add additional software functionality available from the open source community, including making the system see using a webcam, hear using a microphone, and speak using a speaker.You will then learn to use the new hardware capability of the BeagleBone Blue to make your robots move, as well as discover how to add sonar sensors to avoid or find objects. Later, you will learn to remotelycontrol your robot through iOS and Android devices. At the end of this book, you will seehow to integrate all of these functionalities to work together, before developing the most impressive robotics projects: Drone and Submarine.

Deep Learning with Theano. Perform large-scale numerical and scientific computations efficiently

Christopher Bourez

This book offers a complete overview of Deep Learning with Theano, a Python-based library that makes optimizing numerical expressions and deep learning models easy on CPU or GPU.The book provides some practical code examples that help the beginner understand how easy it is to build complex neural networks, while more experimented data scientists will appreciate the reach of the book, addressing supervised and unsupervised learning, generative models, reinforcement learning in the fields of image recognition, natural language processing, or game strategy.The book also discusses image recognition tasks that range from simple digit recognition, image classification, object localization, image segmentation, to image captioning. Natural language processing examples include text generation, chatbots, machine translation, and question answering. The last example deals with generating random data that looks real and solving games such as in the Open-AI gym. At the end, this book sums up the best -performing nets for each task. While early research results were based on deep stacks of neural layers, in particular, convolutional layers, the book presents the principles that improved the efficiency of these architectures, in order to help the reader build new custom nets.