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Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA: Effective techniques for processing complex image data in real time using GPUs
by Bhaumik VaidyaDiscover how CUDA allows OpenCV to handle complex and rapidly growing image data processing in computer and machine vision by accessing the power of GPUKey FeaturesExplore examples to leverage the GPU processing power with OpenCV and CUDAEnhance the performance of algorithms on embedded hardware platformsDiscover C++ and Python libraries for GPU accelerationBook DescriptionComputer vision has been revolutionizing a wide range of industries, and OpenCV is the most widely chosen tool for computer vision with its ability to work in multiple programming languages. Nowadays, in computer vision, there is a need to process large images in real time, which is difficult to handle for OpenCV on its own. This is where CUDA comes into the picture, allowing OpenCV to leverage powerful NVDIA GPUs. This book provides a detailed overview of integrating OpenCV with CUDA for practical applications. To start with, you’ll understand GPU programming with CUDA, an essential aspect for computer vision developers who have never worked with GPUs. You’ll then move on to exploring OpenCV acceleration with GPUs and CUDA by walking through some practical examples.Once you have got to grips with the core concepts, you’ll familiarize yourself with deploying OpenCV applications on NVIDIA Jetson TX1, which is popular for computer vision and deep learning applications. The last chapters of the book explain PyCUDA, a Python library that leverages the power of CUDA and GPUs for accelerations and can be used by computer vision developers who use OpenCV with Python.By the end of this book, you’ll have enhanced computer vision applications with the help of this book's hands-on approach.What you will learnUnderstand how to access GPU device properties and capabilities from CUDA programsLearn how to accelerate searching and sorting algorithmsDetect shapes such as lines and circles in imagesExplore object tracking and detection with algorithmsProcess videos using different video analysis techniques in Jetson TX1Access GPU device properties from the PyCUDA programUnderstand how kernel execution worksWho this book is forThis book is a go-to guide for you if you are a developer working with OpenCV and want to learn how to process more complex image data by exploiting GPU processing. A thorough understanding of computer vision concepts and programming languages such as C++ or Python is expected.
Hands-On GPU Programming with Python and CUDA: Explore High-performance Parallel Computing With Cuda
by Brian TuomanenThis book is for Python developers who want to learn effective GPU programming with CUDA to achieve high performance and boost the productivity of applications. The readers should have an understanding of basic mathematical concepts necessary and an introductory background about any C-based programming language (C, C++, Java, C#, and so forth.)
Hands-On GUI Programming with C++ and Qt5: Build stunning cross-platform applications and widgets with the most powerful GUI framework
by Lee Zhi EngCreate visually appealing and feature-rich applications by using Qt 5 and the C++ languageKey FeaturesExplore Qt 5’s powerful features to easily design your GUI applicationLeverage Qt 5 to build attractive cross-platform applicationsWork with Qt modules for multimedia, networking, and location, to customize your Qt applicationsBook DescriptionQt 5, the latest version of Qt, enables you to develop applications with complex user interfaces for multiple targets. It provides you with faster and smarter ways to create modern UIs and applications for multiple platforms. This book will teach you to design and build graphical user interfaces that are functional, appealing, and user-friendly.In the initial part of the book, you will learn what Qt 5 is and what you can do with it. You will explore the Qt Designer, discover the different types of widgets generally used in Qt 5, and then connect your application to the database to perform dynamic operations. Next, you will be introduced to Qt 5 chart which allows you to easily render different types of graphs and charts and incorporate List View Widgets in your application. You will also work with various Qt modules, like QtLocation, QtWebEngine, and the networking module through the course of the book. Finally, we will focus on cross-platform development with QT 5 that enables you to code once and run it everywhere, including mobile platforms. By the end of this book, you will have successfully learned about high-end GUI applications and will be capable of building many more powerful, cross-platform applications.What you will learnImplement tools provided by Qt 5 to design a beautiful GUIUnderstand different types of graphs and charts supported by Qt 5Create a web browser using the Qt 5 WebEngine module and web view widgetConnect to the MySQL database and display data obtained from it onto the Qt 5 GUIIncorporate the Qt 5 multimedia and networking module in your applicationDevelop Google Map-like applications using Qt 5’s location moduleDiscover cross-platform development by exporting the Qt 5 application to different platformsUncover the secrets behind debugging Qt 5 and C++ applicationsWho this book is forThis book will appeal to developers and programmers who would like to build GUI-based applications. Basic knowledge of C++ is necessary and the basics of Qt would be helpful.
Hands-On High Performance with Spring 5: Techniques for scaling and optimizing Spring and Spring Boot applications
by Chintan Mehta Subhash Shah Pritesh Shah Prashant Goswami Dinesh RadadiyaA hands-on guide to creating, monitoring, and tuning a high performance Spring web applicationKey FeaturesUnderstand common performance pitfalls and improve your application's performanceBuild and deploy strategies for complex applications using the microservice architectureUnderstand internals of JVM - the core of all Java Runtime EnvironmentsBook DescriptionWhile writing an application, performance is paramount. Performance tuning for real-world applications often involves activities geared toward detecting bottlenecks. The recent release of Spring 5.0 brings major advancements in the rich API provided by the Spring framework, which means developers need to master its tools and techniques to achieve high performance applications.Hands-On High Performance with Spring 5 begins with the Spring framework's core features, exploring the integration of different Spring projects. It proceeds to evaluate various Spring specifications to identify those adversely affecting performance. You will learn about bean wiring configurations, aspect-oriented programming, database interaction, and Hibernate to focus on the metrics that help identify performance bottlenecks. You will also look at application monitoring, performance optimization, JVM internals, and garbage collection optimization. Lastly, the book will show you how to leverage the microservice architecture to build a high performance and resilient application.By the end of the book, you will have gained an insight into various techniques and solutions to build and troubleshoot high performance Spring-based applications.What you will learnMaster programming best practices and performance improvement with bean wiringAnalyze the performance of various AOP implementationsExplore database interactions with Spring to optimize design and configurationSolve Hibernate performance issues and trapsLeverage multithreading and concurrent programming to improve application performanceGain a solid foundation in JVM performance tuning using various toolsLearn the key concepts of the microservice architecture and how to monitor themPerform Spring Boot performance tuning, monitoring, and health checksWho this book is forIf you’re a Spring developer who’d like to build high performance applications and have more control over your application's performance in production and development, this book is for you. Some familiarity with Java, Maven, and Eclipse is necessary.
Hands-On Image Processing with Python: Expert techniques for advanced image analysis and effective interpretation of image data
by Sandipan DeyThis book is for Computer Vision Engineers, Image processing Engineers, Software Engineers, ML Engineers who are good with Python programming and wants to explore details and complexities of image processing.
Hands-On Industrial Internet of Things: Create a powerful Industrial IoT infrastructure using Industry 4.0
by Giacomo Veneri Antonio CapassoThis book is targeted towards IoT architects, developers, or any stakeholders working with architectural aspects of Industrial internet of things.
Hands-On Intelligent Agents with OpenAI Gym: Your guide to developing AI agents using deep reinforcement learning
by Praveen PalanisamyImplement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulatorKey FeaturesExplore the OpenAI Gym toolkit and interface to use over 700 learning tasksImplement agents to solve simple to complex AI problemsStudy learning environments and discover how to create your ownBook DescriptionMany real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks.Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level.What you will learnExplore intelligent agents and learning environmentsUnderstand the basics of RL and deep RLGet started with OpenAI Gym and PyTorch for deep reinforcement learningDiscover deep Q learning agents to solve discrete optimal control tasksCreate custom learning environments for real-world problemsApply a deep actor-critic agent to drive a car autonomously in CARLAUse the latest learning environments and algorithms to upgrade your intelligent agent development skillsWho this book is forIf you’re a student, game/machine learning developer, or AI enthusiast looking to get started with building intelligent agents and algorithms to solve a variety of problems with the OpenAI Gym interface, this book is for you. You will also find this book useful if you want to learn how to build deep reinforcement learning-based agents to solve problems in your domain of interest. Though the book covers all the basic concepts that you need to know, some working knowledge of Python programming language will help you get the most out of it.
Hands-On Internet of Things with Blynk: Build on the power of Blynk to configure smart devices and build exciting IoT projects
by Pradeeka SeneviratneConnect things to create amazing IoT applications in minutesKey FeaturesUse Blynk cloud and Blynk server to connect devicesBuild IoT applications on Android and iOS platforms A practical guide that will show how to connect devices using Blynk and Raspberry Pi 3 Book DescriptionBlynk, known as the most user-friendly IoT platform, provides a way to build mobile applications in minutes. With the Blynk drag-n-drop mobile app builder, anyone can build amazing IoT applications with minimal resources and effort, on hardware ranging from prototyping platforms such as Arduino and Raspberry Pi 3 to industrial-grade ESP8266, Intel, Sierra Wireless, Particle, Texas Instruments, and a few others.This book uses Raspberry Pi as the main hardware platform and C/C++ to write sketches to build projects. The first part of this book shows how to set up a development environment with various hardware combinations and required software. Then you will build your first IoT application with Blynk using various hardware combinations and connectivity types such as Ethernet and Wi-Fi. Then you'll use and configure various widgets (control, display, notification, interface, time input, and some advanced widgets) with Blynk App Builder to build applications. Towards the end, you will learn how to connect with and use built-in sensors on Android and iOS mobile devices. Finally you will learn how to build a robot that can be controlled with a Blynk app through the Blynk cloud and personal server.By the end of this book, you will have hands-on experience building IoT applications using Blynk.What you will learnBuild devices using Raspberry Pi and various sensors and actuatorsUse Blynk cloud to connect and control devices through the Blynk app builderConnect devices to Blynk cloud and server through Ethernet and Wi-FiMake applications using Blynk app builder on Android and iOS platforms Run Blynk personal server on the Windows, MAC, and Raspberry Pi platforms Who this book is forThis book is targeted at any stakeholder working in the IoT sector who wants to understand how Blynk works and build exciting IoT projects. Prior understanding of Raspberry Pi, C/C++, and electronics is a must.
Hands-On Kotlin for Enterprise Applications using Java EE
by Raghavendra Rao KKotlin for Enterprise Applications using Java EE is for Java EE developers who want to build their enterprise project or application with Kotlin or migrate from Java to Kotlin. Basic knowledge of programming is necessary.
Hands-On Linux Administration on Azure: Explore the essential Linux administration skills you need to deploy and manage Azure-based workloads
by Frederik VosLearn to efficiently run Linux-based workloads in AzureKey FeaturesManage and deploy virtual machines in your Azure environmentExplore various open source tools to integrate automation and orchestrationLeverage Linux features to create, run, and manage containersBook DescriptionAzure’s market share has increased massively and enterprises are adopting it rapidly. Linux is a widely-used operating system and has proven to be one of the most popular workloads on Azure. It has become crucial for Linux administrators and Microsoft professionals to be well versed with the concepts of managing Linux workloads in an Azure environment.Hands-On Linux Administration on Azure starts by introducing you to the fundamentals of Linux and Azure, after which you will explore advanced Linux features and see how they are managed in an Azure environment. Next, with the help of real-world scenarios, you will learn how to deploy virtual machines(VMs) in Azure, along with extending Azure VMs capabilities and managing them efficiently. You will then understand continuous configuration automation and use Ansible, SaltStack and Powershell DSC for orchestration. As you make your way through the chapters, you will understand containers and how they work, along with managing containers and the various tasks you can perform with them. In the concluding chapters, you will cover some Linux troubleshooting techniques on Azure, and you will also be able to monitor Linux in Azure using different open source tools.By the end of this book, you will be able to administer Linux on Azure and make the most of the important tools required for deployment.What you will learnUnderstand why Azure is the ideal solution for your open source workloadsMaster essential Linux skills and learn to find your way around the Linux environmentDeploy Linux in an Azure environmentUse configuration management to manage Linux in AzureManage containers in an Azure environmentEnhance Linux security and use Azure’s identity management systemsAutomate deployment with Azure Resource Manager (ARM) and PowershellEmploy Ansible to manage Linux instances in an Azure cloud environmentWho this book is forHands-On Linux Administration on Azure is for Linux administrators and Microsoft professionals that need to deploy and manage their workloads in Azure. Prior knowledge of Linux and Azure isn't necessary.
Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python
by Stefan JansenHands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.
Hands-On Machine Learning for Cybersecurity: Safeguard your system by making your machines intelligent using the Python ecosystem
by Sinan Ozdemir Soma HalderGet into the world of smart data security using machine learning algorithms and Python libraries Key Features Learn machine learning algorithms and cybersecurity fundamentals Automate your daily workflow by applying use cases to many facets of security Implement smart machine learning solutions to detect various cybersecurity problems Book Description Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not. Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems What you will learn Use machine learning algorithms with complex datasets to implement cybersecurity concepts Implement machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problems Learn to speed up a system using Python libraries with NumPy, Scikit-learn, and CUDA Understand how to combat malware, detect spam, and fight financial fraud to mitigate cyber crimes Use TensorFlow in the cybersecurity domain and implement real-world examples Learn how machine learning and Python can be used in complex cyber issues Who this book is for This book is for the data scientists, machine learning developers, security researchers, and anyone keen to apply machine learning to up-skill computer security. Having some working knowledge of Python and being familiar with the basics of machine learning and cybersecurity fundamentals will help to get the most out of the book
Hands-On Machine Learning on Google Cloud Platform: Implementing smart and efficient analytics using Cloud ML Engine
by Alexis Perrier Giuseppe Ciaburro Kishore AyyadevaraUnleash Google's Cloud Platform to build, train and optimize machine learning modelsKey FeaturesGet well versed in GCP pre-existing services to build your own smart modelsA comprehensive guide covering aspects from data processing, analyzing to building and training ML modelsA practical approach to produce your trained ML models and port them to your mobile for easy accessBook DescriptionGoogle Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions.This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications.By the end of this book, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems.What you will learnUse Google Cloud Platform to build data-based applications for dashboards, web, and mobileCreate, train and optimize deep learning models for various data science problems on big dataLearn how to leverage BigQuery to explore big datasetsUse Google’s pre-trained TensorFlow models for NLP, image, video and much moreCreate models and architectures for Time series, Reinforcement Learning, and generative modelsCreate, evaluate, and optimize TensorFlow and Keras models for a wide range of applicationsWho this book is forThis book is for data scientists, machine learning developers and AI developers who want to learn Google Cloud Platform services to build machine learning applications. Since the interaction with the Google ML platform is mostly done via the command line, the reader is supposed to have some familiarity with the bash shell and Python scripting. Some understanding of machine learning and data science concepts will be handy
Hands-On Machine Learning with Azure: Build powerful models with cognitive machine learning and artificial intelligence
by Jen Stirrup Anindita Basak Thomas K Abraham Parashar Shah Lauri LehmanImplement machine learning, cognitive services, and artificial intelligence solutions by leveraging Azure cloud technologiesKey FeaturesLearn advanced concepts in Azure ML and the Cortana Intelligence Suite architectureExplore ML Server using SQL Server and HDInsight capabilitiesImplement various tools in Azure to build and deploy machine learning modelsBook DescriptionImplementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage. However, Azure has created ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML projects in the cloud in a cost-effective way.The book begins by covering the benefits of ML and AI in the cloud. You will then explore Microsoft’s Team Data Science Process to establish a repeatable process for successful AI development and implementation. You will also gain an understanding of AI technologies available in Azure and the Cognitive Services APIs to integrate them into bot applications. This book lets you explore prebuilt templates with Azure Machine Learning Studio and build a model using canned algorithms that can be deployed as web services. The book then takes you through a preconfigured series of virtual machines in Azure targeted at AI development scenarios. You will get to grips with the ML Server and its capabilities in SQL and HDInsight. In the concluding chapters, you’ll integrate patterns with other non-AI services in Azure.By the end of this book, you will be fully equipped to implement smart cognitive actions in your models.What you will learnDiscover the benefits of leveraging the cloud for ML and AIUse Cognitive Services APIs to build intelligent botsBuild a model using canned algorithms from Microsoft and deploy it as a web serviceDeploy virtual machines in AI development scenariosApply R, Python, SQL Server, and Spark in AzureBuild and deploy deep learning solutions with CNTK, MMLSpark, and TensorFlowImplement model retraining in IoT, Streaming, and Blockchain solutionsExplore best practices for integrating ML and AI functions with ADLA and logic appsWho this book is forIf you are a data scientist or developer familiar with Azure ML and cognitive services and want to create smart models and make sense of data in the cloud, this book is for you. You’ll also find this book useful if you want to bring powerful machine learning services into your cloud applications. Some experience with data manipulation and processing, using languages like SQL, Python, and R, will aid in understanding the concepts covered in this book
Hands-On Machine Learning with C#: Build smart, speedy, and reliable data-intensive applications using machine learning
by Matt R. ColeExplore supervised and unsupervised learning techniques and add smart features to your applicationsKey FeaturesLeverage machine learning techniques to build real-world applicationsUse the Accord.NET machine learning framework for reinforcement learningImplement machine learning techniques using Accord, nuML, and EncogBook DescriptionThe necessity for machine learning is everywhere, and most production enterprise applications are written in C# using tools such as Visual Studio, SQL Server, and Microsoft Azur2e. Hands-On Machine Learning with C# uniquely blends together an understanding of various machine learning concepts, techniques of machine learning, and various available machine learning tools through which users can add intelligent features.These tools include image and motion detection, Bayes intuition, and deep learning, to C# .NET applications.Using this book, you will learn to implement supervised and unsupervised learning algorithms and will be better equipped to create excellent predictive models. In addition, you will learn both supervised and unsupervised forms of regression, mainly logistic and linear regression, in depth. Next, you will use the nuML machine learning framework to learn how to create a simple decision tree. In the concluding chapters, you will use the Accord.Net machine learning framework to learn sequence recognition of handwritten numbers using dynamic time warping. We will also cover advanced concepts such as artificial neural networks, autoencoders, and reinforcement learning.By the end of this book, you will have developed a machine learning mindset and will be able to leverage C# tools, techniques, and packages to build smart, predictive, and real-world business applications.What you will learnLearn to parameterize a probabilistic problemUse Naive Bayes to visually plot and analyze dataPlot a text-based representation of a decision tree using nuMLUse the Accord.NET machine learning framework for associative rule-based learningDevelop machine learning algorithms utilizing fuzzy logicExplore support vector machines for image recognitionUnderstand dynamic time warping for sequence recognitionWho this book is forHands-On Machine Learning with C#is forC# .NETdevelopers who work on a range of platforms from .NET and Windows to mobile devices. Basic knowledge of statistics is required.
Hands-on Machine Learning with JavaScript: Solve complex computational web problems using machine learning
by Burak KanberA definitive guide to creating an intelligent web application with the best of machine learning and JavaScriptKey FeaturesSolve complex computational problems in browser with JavaScriptTeach your browser how to learn from rules using the power of machine learningUnderstand discoveries on web interface and API in machine learningBook DescriptionIn 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.What you will learnGet an overview of state-of-the-art machine learningUnderstand the pre-processing of data handling, cleaning, and preparationLearn Mining and Pattern Extraction with JavaScriptBuild your own model for classification, clustering, and predictionIdentify the most appropriate model for each type of problemApply machine learning techniques to real-world applicationsLearn how JavaScript can be a powerful language for machine learningWho this book is forThis 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.
Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem
by Ankur Ankan Abinash PandaUnleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearnKey FeaturesBuild a variety of Hidden Markov Models (HMM)Create and apply models to any sequence of data to analyze, predict, and extract valuable insightsUse natural language processing (NLP) techniques and 2D-HMM model for image segmentationBook DescriptionHidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone.Once you’ve covered the basic concepts of Markov chains, you’ll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you’ll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you’ll explore the Bayesian approach of inference and learn how to apply it in HMMs.In further chapters, you’ll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You’ll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you’ll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading.By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects.What you will learnExplore a balance of both theoretical and practical aspects of HMMImplement HMMs using different datasets in Python using different packagesUnderstand multiple inference algorithms and how to select the right algorithm to resolve your problemsDevelop a Bayesian approach to inference in HMMsImplement HMMs in finance, natural language processing (NLP), and image processingDetermine the most likely sequence of hidden states in an HMM using the Viterbi algorithmWho this book is forHands-On Markov Models with Python is for you if you are a data analyst, data scientist, or machine learning developer and want to enhance your machine learning knowledge and skills. This book will also help you build your own hidden Markov models by applying them to any sequence of data.Basic knowledge of machine learning and the Python programming language is expected to get the most out of the book
Hands-On Meta Learning with Python
by Sudharsan RavichandiranThe book will help machine learning enthusiasts, AI researchers, and data scientists who want to learn meta learning as an advanced approach for training the machine learning models. The book assumes a working knowledge of Machine learning concepts and sound knowledge of Python programming
Hands-On Microservices – Monitoring and Testing: A performance engineer's guide to the continuous testing and monitoring of microservices
by Dinesh RajputLearn and implement various techniques related to testing, monitoring and optimization for microservices architecture. Key Features *Learn different approaches for testing microservices to design and implement, robust and secure applications *Become more efficient while working with microservices *Explore Testing and Monitoring tools such as JMeter, Ready API,and AppDynamics Book Description 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. What you will learn *Understand the architecture of microservices and how to build services *Establish how external APIs help to accelerate the development process *Understand testing techniques, such as unit testing, integration testing, end-to-end testing, and UI/functional testing *Explore various tools related to the performance testing, monitoring, and optimization of microservices *Design strategies for performance testing *Identify performance issues and fine-tune performance Who this book is for This book is for developers who are involved with microservices architecture to develop robust and secure applications. Basic knowledge of microservices is essential in order to get the most out of this book.
Hands-On Microservices with C#: Designing a real-worl, enterprise-grade microservice ecosystem with the efficiency of C# 7
by Matt R. ColeBuild enterprise-grade microservice ecosystems with intensive case studies using C#Key FeaturesLearn to build message-based microservicesPacked with case studies to explain the intricacies of large-scale microservicesBuild scalable, modular, and robust architectures with C#Book DescriptionC# is a powerful language when it comes to building applications and software architecture using rich libraries and tools such as .NET.This book will harness the strength of C# in developing microservices architectures and applications.This book shows developers how to develop an enterprise-grade, event-driven, asynchronous, message-based microservice framework using C#, .NET, and various open source tools. We will discuss how to send and receive messages, how to design many types of microservice that are truly usable in a corporate environment. We will also dissect each case and explain the code, best practices, pros and cons, and more.Through our journey, we will use many open source tools, and create file monitors, a machine learning microservice, a quantitative financial microservice that can handle bonds and credit default swaps, a deployment microservice to show you how to better manage your deployments, and memory, health status, and other microservices. By the end of this book, you will have a complete microservice ecosystem you can place into production or customize in no time.What you will learnExplore different open source tools within the context of designing microservicesLearn to provide insulation to exception-prone function callsBuild common messages used between microservices for communicationLearn to create a microservice using our base class and interfaceDesign a quantitative financial machine microserviceLearn to design a microservice that is capable of using Blockchain technology Who this book is forC# developers, software architects, and professionals who want to master the art of designing the microservice architecture that is scalable based on environment. Developers should have a basic understanding of.NET application development using C# and Visual Studio
Hands-On Microservices with Kotlin: Build reactive and cloud-native microservices with Kotlin using Spring 5 and Spring Boot 2.0
by Juan Antonio Medina IglesiasBuild smart, efficient, and fast enterprise-grade web implementation of the microservices architecture that can be easily scaled. Key Features Write easy-to-maintain lean and clean code with Kotlin for developing better microservices Scale your Microserivces in your own cloud with Docker and Docker Swarm Explore Spring 5 functional reactive web programming with Spring WebFlux Book Description With Google's inclusion of first-class support for Kotlin in their Android ecosystem, Kotlin's future as a mainstream language is assured. Microservices help design scalable, easy-to-maintain web applications; Kotlin allows us to take advantage of modern idioms to simplify our development and create high-quality services. With 100% interoperability with the JVM, Kotlin makes working with existing Java code easier. Well-known Java systems such as Spring, Jackson, and Reactor have included Kotlin modules to exploit its language features. This book guides the reader in designing and implementing services, and producing production-ready, testable, lean code that's shorter and simpler than a traditional Java implementation. Reap the benefits of using the reactive paradigm and take advantage of non-blocking techniques to take your services to the next level in terms of industry standards. You will consume NoSQL databases reactively to allow you to create high-throughput microservices. Create cloud-native microservices that can run on a wide range of cloud providers, and monitor them. You will create Docker containers for your microservices and scale them. Finally, you will deploy your microservices in OpenShift Online. What you will learn Understand microservice architectures and principles Build microservices in Kotlin using Spring Boot 2.0 and Spring Framework 5.0 Create reactive microservices that perform non-blocking operations with Spring WebFlux Use Spring Data to get data reactively from MongoDB Test effectively with JUnit and Kotlin Create cloud-native microservices with Spring Cloud Build and publish Docker images of your microservices Scaling microservices with Docker Swarm Monitor microservices with JMX Deploy microservices in OpenShift Online Who this book is for If you are a Kotlin developer with a basic knowledge of microservice architectures and now want to effectively implement these services on enterprise-level web applications, then this book is for you
Hands-On Microservices with Node.js: Build, test, and deploy robust microservices in JavaScript
by Diogo ResendeLearn essential microservices concepts while developing scalable applications with Express, Docker, Kubernetes, and Docker Swarm using Node 10Key FeaturesWrite clean and maintainable code with JavaScript for better microservices developmentDive into the Node.js ecosystem and build scalable microservices with Seneca, Hydra, and Express.jsDevelop smart, efficient, and fast enterprise-grade microservices implementationBook DescriptionMicroservices enable us to develop software in small pieces that work together but can be developed separately; this is one reason why enterprises have started embracing them. For the past few years, Node.js has emerged as a strong candidate for developing microservices because of its ability to increase your productivity and the performance of your applications.Hands-On Microservices with Node.js is an end-to-end guide on how to dismantle your monolithic application and embrace the microservice architecture - right from architecting your services and modeling them to integrating them into your application. We'll develop and deploy these microservices using Docker. Scalability is an important factor to consider when adding more functionality to your application, and so we delve into various solutions, such as Docker Swarm and Kubernetes, to scale our microservices. Testing and deploying these services while scaling is a real challenge; we'll overcome this challenge by setting up deployment pipelines that break up application build processes in several stages. Later on, we'll take a look at serverless architecture for our microservices and its benefits against traditional architecture. Finally, we share best practices and several design patterns for creating efficient microservices.What you will learnLearn microservice conceptsExplore different service architectures, such as Hydra and SenecaUnderstand how to use containers and the process of testingUse Docker and Swarm for continuous deployment and scalingLearn how to geographically spread your microservicesDeploy a cloud-native microservice to an online providerKeep your microservice independent of online providersWho this book is forThis book is for JavaScript developers seeking to utilize their skills to build microservices and move away from the monolithic architecture. Prior knowledge of Node.js is assumed.
Hands-On Microsoft® Windows® Server 2016
by Michael PalmerDiscover the perfect resource for learning Windows Server 2016 from the ground up with HANDS-ON MICROSOFT WINDOWS SERVER 2016. Designed to build a foundation in basic server administration, this book requires no previous server experience. It covers all of the critical Windows Server 2016 features, including the advantages unique to this new server operating system. You learn how to choose the right server edition for your needs. You also learn to install, configure, customize, manage, and troubleshoot your server most effectively. If you are new to server administration, this book gives you the background and knowledge you need to manage servers on small to large networks. If you are an experienced server administrator, this book provides a fast way to get up to speed on the latest Windows Server 2016 administration.
Hands-On MQTT Programming with Python: Work with the lightweight IoT protocol in Python
by Gastón C. HillarExplore the features included in the latest versions of MQTT for IoT and M2M communications and use them with modern Python 3.Key FeaturesMake your connected devices less prone to attackers by understanding security mechanismsTake advantage of MQTT features for IoT and Machine-to-Machine communicationsThe only book that covers MQTT with a single language, PythonBook DescriptionMQTT 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.What you will learnLearn how MQTT and its lightweight messaging system workUnderstand the MQTT puzzle: clients, servers (formerly known as brokers), and connectionsExplore the features included in the latest versions of MQTT for IoT and M2M communicationsPublish and receive MQTT messages with PythonLearn the difference between blocking and threaded network loopsTake advantage of the last will and testament featureWork with cloud-based MQTT interfaces in PythonWho this book is forThis 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.
Hands-on Natural Language Processing with Python: A practical guide to applying deep learning architectures to your NLP applications
by Rajalingappaa Shanmugmani Karthik Muthusamy Rajesh ArumugamIf you are a developer and want to build a deep learning application that leverages Natural Language Processing (NLP) techniques. All you need is the basics of machine learning and Python to decode the entire book.