Artificial Intelligence For Iot Cookbook

Author: Michael Roshak
Editor: Packt Publishing Ltd
ISBN: 1838986499
File Size: 48,20 MB
Format: PDF, Docs
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Implement machine learning and deep learning techniques to perform predictive analytics on real-time IoT data Key Features Discover quick solutions to common problems that you'll face while building smart IoT applications Implement advanced techniques such as computer vision, NLP, and embedded machine learning Build, maintain, and deploy machine learning systems to extract key insights from IoT data Book Description Artificial intelligence (AI) is rapidly finding practical applications across a wide variety of industry verticals, and the Internet of Things (IoT) is one of them. Developers are looking for ways to make IoT devices smarter and to make users' lives easier. With this AI cookbook, you'll be able to implement smart analytics using IoT data to gain insights, predict outcomes, and make informed decisions, along with covering advanced AI techniques that facilitate analytics and learning in various IoT applications. Using a recipe-based approach, the book will take you through essential processes such as data collection, data analysis, modeling, statistics and monitoring, and deployment. You'll use real-life datasets from smart homes, industrial IoT, and smart devices to train and evaluate simple to complex models and make predictions using trained models. Later chapters will take you through the key challenges faced while implementing machine learning, deep learning, and other AI techniques, such as natural language processing (NLP), computer vision, and embedded machine learning for building smart IoT systems. In addition to this, you'll learn how to deploy models and improve their performance with ease. By the end of this book, you'll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems. What you will learn Explore various AI techniques to build smart IoT solutions from scratch Use machine learning and deep learning techniques to build smart voice recognition and facial detection systems Gain insights into IoT data using algorithms and implement them in projects Perform anomaly detection for time series data and other types of IoT data Implement embedded systems learning techniques for machine learning on small devices Apply pre-trained machine learning models to an edge device Deploy machine learning models to web apps and mobile using TensorFlow.js and Java Who this book is for If you're an IoT practitioner looking to incorporate AI techniques to build smart IoT solutions without having to trawl through a lot of AI theory, this AI IoT book is for you. Data scientists and AI developers who want to build IoT-focused AI solutions will also find this book useful. Knowledge of the Python programming language and basic IoT concepts is required to grasp the concepts covered in this artificial intelligence book more effectively.

Tensorflow Machine Learning Cookbook

Author: Nick McClure
Editor: Packt Publishing Ltd
ISBN: 1786466309
File Size: 67,58 MB
Format: PDF, ePub, Mobi
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Explore machine learning concepts using the latest numerical computing library — TensorFlow — with the help of this comprehensive cookbook About This Book Your quick guide to implementing TensorFlow in your day-to-day machine learning activities Learn advanced techniques that bring more accuracy and speed to machine learning Upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow Who This Book Is For This book is ideal for data scientists who are familiar with C++ or Python and perform machine learning activities on a day-to-day basis. Intermediate and advanced machine learning implementers who need a quick guide they can easily navigate will find it useful. What You Will Learn Become familiar with the basics of the TensorFlow machine learning library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks and improve predictions Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Take TensorFlow into production In Detail TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google's machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production. Style and approach This book takes a recipe-based approach where every topic is explicated with the help of a real-world example.

Machine Learning For Cybersecurity Cookbook

Author: Emmanuel Tsukerman
Editor: Packt Publishing Ltd
ISBN: 1838556346
File Size: 11,63 MB
Format: PDF, Kindle
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Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key Features Manage data of varying complexity to protect your system using the Python ecosystem Apply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and social engineering Automate your daily workflow by addressing various security challenges using the recipes covered in the book Book Description Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers. You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach. What you will learn Learn how to build malware classifiers to detect suspicious activities Apply ML to generate custom malware to pentest your security Use ML algorithms with complex datasets to implement cybersecurity concepts Create neural networks to identify fake videos and images Secure your organization from one of the most popular threats – insider threats Defend against zero-day threats by constructing an anomaly detection system Detect web vulnerabilities effectively by combining Metasploit and ML Understand how to train a model without exposing the training data Who this book is for This book is for cybersecurity professionals and security researchers who are looking to implement the latest machine learning techniques to boost computer security, and gain insights into securing an organization using red and blue team ML. This recipe-based book will also be useful for data scientists and machine learning developers who want to experiment with smart techniques in the cybersecurity domain. Working knowledge of Python programming and familiarity with cybersecurity fundamentals will help you get the most out of this book.

Azure Iot Development Cookbook

Author: Yatish Patil
Editor: Packt Publishing Ltd
ISBN: 1787280098
File Size: 18,68 MB
Format: PDF, Mobi
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Over 50 recipes to drive IoT innovation with Microsoft Azure About This Book Build secure and scalable IoT solutions with Azure IoT platform Learn techniques to build end to end IoT solutions leveraging the Azure IoT platform Filled with practical recipes to help you increase connectivity and automation across IoT devices Who This Book Is For If you are an application developer and want to build robust and secure IoT solution for your organization using Azure IoT, then this book is for you. What You Will Learn Build IoT Solutions using Azure IoT & Services Learn device configuration and communication protocols Understand IoT Suite and Pre-configured solutions Manage Secure Device communications Understand Device management, alerts Introduction with IoT Analytics, reference IoT Architectures Reference Architectures from Industry Pre-Configured IoT Suite solutions In Detail Microsoft's end-to-end IoT platform is the most complete IoT offering, empowering enterprises to build and realize value from IoT solutions efficiently. It is important to develop robust and reliable solutions for your organization to leverage IoT services. This book focuses on how to start building custom solutions using the IoT hub or the preconfigured solution of Azure IoT suite. As a developer, you will be taught how to connect multiple devices to the Azure IoT hub, develop, manage the IoT hub service and integrate the hub with cloud. We will be covering REST APIs along with HTTP, MQTT and AMQP protocols. It also helps you learn Pre-Configured IoT Suite solution. Moving ahead we will be covering topics like:-Process device-to-cloud messages and cloud-to-device messages using .Net-Direct methods and device management-Query Language, Azure IoT SDK for .Net-Creating and managing, Securing IoT hub, IoT Suite and many more. We will be using windows 10 IoT core, Visual Studio, universal Windows platform. At the end, we will take you through IoT analytics and provide a demo of connecting real device with Azure IoT. Style and approach A set of exciting recipes of using Microsoft Azure IoT more effectively.

Ensemble Machine Learning Cookbook

Author: Dipayan Sarkar
Editor: Packt Publishing Ltd
ISBN: 1789132509
File Size: 45,45 MB
Format: PDF, ePub, Mobi
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Implement machine learning algorithms to build ensemble models using Keras, H2O, Scikit-Learn, Pandas and more Key Features Apply popular machine learning algorithms using a recipe-based approach Implement boosting, bagging, and stacking ensemble methods to improve machine learning models Discover real-world ensemble applications and encounter complex challenges in Kaggle competitions Book Description Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking. The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you’ll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The book will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You’ll also be able to implement models such as fraud detection, text categorization, and sentiment analysis. By the end of this book, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes. What you will learn Understand how to use machine learning algorithms for regression and classification problems Implement ensemble techniques such as averaging, weighted averaging, and max-voting Get to grips with advanced ensemble methods, such as bootstrapping, bagging, and stacking Use Random Forest for tasks such as classification and regression Implement an ensemble of homogeneous and heterogeneous machine learning algorithms Learn and implement various boosting techniques, such as AdaBoost, Gradient Boosting Machine, and XGBoost Who this book is for This book is designed for data scientists, machine learning developers, and deep learning enthusiasts who want to delve into machine learning algorithms to build powerful ensemble models. Working knowledge of Python programming and basic statistics is a must to help you grasp the concepts in the book.

Apache Spark 2 X Machine Learning Cookbook

Author: Siamak Amirghodsi
Editor: Packt Publishing Ltd
ISBN: 1782174605
File Size: 29,43 MB
Format: PDF, ePub, Docs
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Simplify machine learning model implementations with Spark About This Book Solve the day-to-day problems of data science with Spark This unique cookbook consists of exciting and intuitive numerical recipes Optimize your work by acquiring, cleaning, analyzing, predicting, and visualizing your data Who This Book Is For This book is for Scala developers with a fairly good exposure to and understanding of machine learning techniques, but lack practical implementations with Spark. A solid knowledge of machine learning algorithms is assumed, as well as hands-on experience of implementing ML algorithms with Scala. However, you do not need to be acquainted with the Spark ML libraries and ecosystem. What You Will Learn Get to know how Scala and Spark go hand-in-hand for developers when developing ML systems with Spark Build a recommendation engine that scales with Spark Find out how to build unsupervised clustering systems to classify data in Spark Build machine learning systems with the Decision Tree and Ensemble models in Spark Deal with the curse of high-dimensionality in big data using Spark Implement Text analytics for Search Engines in Spark Streaming Machine Learning System implementation using Spark In Detail Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we'll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems. Style and approach This book is packed with intuitive recipes supported with line-by-line explanations to help you understand how to optimize your work flow and resolve problems when working with complex data modeling tasks and predictive algorithms. This is a valuable resource for data scientists and those working on large scale data projects.

Deep Learning With R Cookbook

Author: Swarna Gupta
Editor: Packt Publishing Ltd
ISBN: 1789808278
File Size: 30,71 MB
Format: PDF, ePub
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Tackle the complex challenges faced while building end-to-end deep learning models using modern R libraries Key Features Understand the intricacies of R deep learning packages to perform a range of deep learning tasks Implement deep learning techniques and algorithms for real-world use cases Explore various state-of-the-art techniques for fine-tuning neural network models Book Description Deep learning (DL) has evolved in recent years with developments such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning. This book will get you up and running with R 3.5.x to help you implement DL techniques. The book starts with the various DL techniques that you can implement in your apps. A unique set of recipes will help you solve binomial and multinomial classification problems, and perform regression and hyperparameter optimization. To help you gain hands-on experience of concepts, the book features recipes for implementing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Long short-term memory (LSTMs) networks, as well as sequence-to-sequence models and reinforcement learning. You’ll then learn about high-performance computation using GPUs, along with learning about parallel computation capabilities in R. Later, you’ll explore libraries, such as MXNet, that are designed for GPU computing and state-of-the-art DL. Finally, you’ll discover how to solve different problems in NLP, object detection, and action identification, before understanding how to use pre-trained models in DL apps. By the end of this book, you’ll have comprehensive knowledge of DL and DL packages, and be able to develop effective solutions for different DL problems. What you will learn Work with different datasets for image classification using CNNs Apply transfer learning to solve complex computer vision problems Use RNNs and their variants such as LSTMs and Gated Recurrent Units (GRUs) for sequence data generation and classification Implement autoencoders for DL tasks such as dimensionality reduction, denoising, and image colorization Build deep generative models to create photorealistic images using GANs and VAEs Use MXNet to accelerate the training of DL models through distributed computing Who this book is for This deep learning book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to learn key tasks in deep learning domains using a recipe-based approach. A strong understanding of machine learning and working knowledge of the R programming language is mandatory.

Cookbook For Mobile Robotic Platform Control

Author: Dr. Anita Gehlot
Editor: BPB Publications
ISBN: 9388511670
File Size: 17,40 MB
Format: PDF, ePub, Docs
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Controlling Robots using Blynk, Virtuino, Cayenne, Thingspeak, Firebase DESCRIPTION This book provides a platform to the readers, where they can understand the applications of ‘Internet of Things’ to control the robotic platform. It covers the basic knowledge of the mobile apps with their designing steps and programming. The objective of the book is to discuss various applications of robotic platform where ‘Internet of things’ can play an important role. This book comprises of total seventeen chapters for designing different independent prototypes for the various control methods. It covers introduction to IoT and basic components to design a robotic platform. The system demonstration is done with the help of Ti Launch Pad and other interfacing devices. The control of robot with different mobile apps like Blynk, Virtuino, Cayenne, Thingspeak, Firebase are included for vast coverage of scope. It would be beneficial for the people who want to get started with hardware based robotic prototypes with IoT. This book is entirely based on the practical experience of the authors while undergoing projects with the students and industries. KEY FEATURES The book provides gradual pace of basics to advanced interfacing and programming with Ti launch pad for IoT applications. It provides a unique style for IoT applications with program codes. It discusses various applications where the Internet of Things plays an important role, and considers a number of different independent prototypes for various mobile robotics platform control methods. The control of robot with different mobile apps like Blynk, Virtuino, Cayenne, Thingspeak, Firebase are included for vast coverage of scope. Step by step programming, to get started with Ti launch Pad Case studies to provide solution to real time problems The case studies and programming in book are tested on real hardware during handling the industrial and student projects. WHAT WILL YOU LEARN Interfacing of Ti launch Pad and NodeMCU with Input/Output Devices Serial Communication between Ti Launch Pad and NodeMCU Robot Control Using the Blynk, Virtuino App Environment Monitoring Robot with BLYNK App Sensory Data Acquisition Robot Using a ThingSpeak Server Robot Control with Cayenne App, Local Server and NodeMCU, Firebase Server WHO THIS BOOK IS FOR Students pursuing BE/BSc/ME/MSc/BTech/MTech in Computer Science, Electronics, Electrical. Table of Contents 1. Introduction 2. Components of a Robotic Platform 3. Interfacing of Ti launch Pad with Input/Output Devices 4. Interfacing of NodeMCU with Input/Output Devices 5. Serial Communication between Ti Launch Pad and NodeMCU 6. Robot Control Using the Blynk App 7. Robot Control Using the Virtuino App 8. Environment Monitoring Robot with BLYNK App 9. Sensory Data Acquisition Robot Using a ThingSpeak Server 10. Robot Control with Cayenne App 11. Robot Control with Local Server and NodeMCU 12. Robot Control with a Firebase Server 13. XBee and Wi-Fi Modem Based Robot Control 14. Fire Fighting Robot 15. The Internet of Things Robotic Arm 16. The Smart Orchard with a Robotic Arm Sprinkler 17. Smart Farming with the IoT

Delphi Cookbook

Author: Daniele Spinetti
Editor: Packt Publishing Ltd
ISBN: 1788623185
File Size: 51,62 MB
Format: PDF, ePub
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Quickly learn and employ practical recipes for developing real-world, cross-platform applications using Delphi. Key Features Get to grips with Delphi to build and deploy various cross-platform applications Design and deploy real-world apps by implementing a single source codebase Build robust and optimized GUI applications with ease Book Description Delphi is a cross-platform integrated development environment (IDE) that supports rapid application development on different platforms, saving you the pain of wandering amid GUI widget details or having to tackle inter-platform incompatibilities. Delphi Cookbook begins with the basics of Delphi and gets you acquainted with JSON format strings, XSLT transformations, Unicode encodings, and various types of streams. You’ll then move on to more advanced topics such as developing higher-order functions and using enumerators and run-time type information (RTTI). As you make your way through the chapters, you’ll understand Delphi RTL functions, use FireMonkey in a VCL application, and cover topics such as multithreading, using aparallel programming library and deploying Delphi on a server. You’ll take a look at the new feature of WebBroker Apache modules, join the mobile revolution with FireMonkey, and learn to build data-driven mobile user interfaces using the FireDAC database access framework. This book will also show you how to integrate your apps with Internet of Things (IoT). By the end of the book, you will have become proficient in Delphi by exploring its different aspects such as building cross-platforms and mobile applications, designing server-side programs, and integrating these programs with IoT. What you will learn Develop visually stunning applications using FireMonkey Deploy LiveBinding effectively with the right object-oriented programming (OOP) approach Create RESTful web services that run on Linux or Windows Build mobile apps that read data from a remote server efficiently Call platform native API on Android and iOS for an unpublished API Manage software customization by making better use of an extended RTTI Integrate your application with IOT Who this book is for Delphi Cookbook is for intermediate developers with a basic knowledge of Delphi who want to discover and understand all the development possibilities offered by it.

Neuronale Netze Selbst Programmieren

Author: Tariq Rashid
Editor:
ISBN: 9781492064046
File Size: 11,40 MB
Format: PDF, ePub
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Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Dennoch verstehen nur wenige, wie Neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie Neuronale Netze arbeiten. Dafür brauchen Sie keine tieferen Mathematik-Kenntnisse, denn alle mathematischen Konzepte werden behutsam und mit vielen Illustrationen erläutert. Dann geht es in die Praxis: Sie programmieren Ihr eigenes Neuronales Netz mit Python und bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. Zum Schluss lassen Sie das Netz noch auf einem Raspberry Pi Zero laufen. - Tariq Rashid hat eine besondere Fähigkeit, schwierige Konzepte verständlich zu erklären, dadurch werden Neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.

Apache Spark 2 Data Processing And Real Time Analytics

Author: Romeo Kienzler
Editor: Packt Publishing Ltd
ISBN: 1789959918
File Size: 34,96 MB
Format: PDF, Kindle
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Build efficient data flow and machine learning programs with this flexible, multi-functional open-source cluster-computing framework Key Features Master the art of real-time big data processing and machine learning Explore a wide range of use-cases to analyze large data Discover ways to optimize your work by using many features of Spark 2.x and Scala Book Description Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: Mastering Apache Spark 2.x by Romeo Kienzler Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook What you will learn Get to grips with all the features of Apache Spark 2.x Perform highly optimized real-time big data processing Use ML and DL techniques with Spark MLlib and third-party tools Analyze structured and unstructured data using SparkSQL and GraphX Understand tuning, debugging, and monitoring of big data applications Build scalable and fault-tolerant streaming applications Develop scalable recommendation engines Who this book is for If you are an intermediate-level Spark developer looking to master the advanced capabilities and use-cases of Apache Spark 2.x, this Learning Path is ideal for you. Big data professionals who want to learn how to integrate and use the features of Apache Spark and build a strong big data pipeline will also find this Learning Path useful. To grasp the concepts explained in this Learning Path, you must know the fundamentals of Apache Spark and Scala.

Enterprise Digitization Patterns

Author: Srikanth Narasimhan, Jagadish Chundury
Editor: Notion Press
ISBN: 1948473917
File Size: 47,18 MB
Format: PDF, ePub, Mobi
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Digitization and Artificial Intelligence are at the center of every board room conversation these days. Most CEOs, senior management and boards are less worried about their traditional competitors. The impact of disruption through digitization is real and quantifiable – 52% of Fortune 500 companies have been replaced since 2000. The task of enabling new digital business models gets exponentially harder as the complexity of systems are greater. Most CIOs, CTOs are struggling with when to start, what to do, and how to meet the expectations of their CEOs and Boards. Design patterns help narrow this gap by documenting a well-working solution to a problem that occurs repeatedly in a given context. “Enterprise Digitization Patterns” breaks down digital disruption enablers and delivers a cookbook across three key pillars – Digital Experience, Enterprise IoT and Autonomous Systems. The book provides reference architectures, design patterns, maturity models and practical case studies to drive new forms of customer value, business outcomes and business models. The design patterns are distinct or relevant to modern-day enterprise digital platforms that enables enterprise digital business models.

Apache Spark 2

Author: Romeo Kienzler
Editor:
ISBN: 9781789959208
File Size: 52,18 MB
Format: PDF, ePub, Docs
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Build efficient data flow and machine learning programs with this flexible, multi-functional open-source cluster-computing framework Key Features Master the art of real-time big data processing and machine learning Explore a wide range of use-cases to analyze large data Discover ways to optimize your work by using many features of Spark 2.x and Scala Book Description Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: Mastering Apache Spark 2.x by Romeo Kienzler Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook What you will learn Get to grips with all the features of Apache Spark 2.x Perform highly optimized real-time big data processing Use ML and DL techniques with Spark MLlib and third-party tools Analyze structured and unstructured data using SparkSQL and GraphX Understand tuning, debugging, and monitoring of big data applications Build scalable and fault-tolerant streaming applications Develop scalable recommendation engines Who this book is for If you are an intermediate-level Spark developer looking to master the advanced capabilities and use-cases of Apache Spark 2.x, this Learning Path is ideal for you. Big data professionals who want to learn how to integrate and use the features of Apache Spark and build a strong big data pipeline will also find this Learning Path useful. To grasp the concepts explained in this Learning Path, you must know the fundamentals of Apache Spark and Scala.

Rust Programming Cookbook

Author: Claus Matzinger
Editor: Packt Publishing Ltd
ISBN: 1789531748
File Size: 51,99 MB
Format: PDF, Mobi
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Practical solutions to overcome challenges in creating console and web applications and working with systems-level and embedded code, network programming, deep neural networks, and much more. Key Features Work through recipes featuring advanced concepts such as concurrency, unsafe code, and macros to migrate your codebase to the Rust programming language Learn how to run machine learning models with Rust Explore error handling, macros, and modularization to write maintainable code Book Description Rust 2018, Rust's first major milestone since version 1.0, brings more advancement in the Rust language. The Rust Programming Cookbook is a practical guide to help you overcome challenges when writing Rust code. This Rust book covers recipes for configuring Rust for different environments and architectural designs, and provides solutions to practical problems. It will also take you through Rust's core concepts, enabling you to create efficient, high-performance applications that use features such as zero-cost abstractions and improved memory management. As you progress, you'll delve into more advanced topics, including channels and actors, for building scalable, production-grade applications, and even get to grips with error handling, macros, and modularization to write maintainable code. You will then learn how to overcome common roadblocks when using Rust for systems programming, IoT, web development, and network programming. Finally, you'll discover what Rust 2018 has to offer for embedded programmers. By the end of the book, you'll have learned how to build fast and safe applications and services using Rust. What you will learn Understand how Rust provides unique solutions to solve system programming language problems Grasp the core concepts of Rust to develop fast and safe applications Explore the possibility of integrating Rust units into existing applications for improved efficiency Discover how to achieve better parallelism and security with Rust Write Python extensions in Rust Compile external assembly files and use the Foreign Function Interface (FFI) Build web applications and services using Rust for high performance Who this book is for The Rust cookbook is for software developers looking to enhance their knowledge of Rust and leverage its features using modern programming practices. Familiarity with Rust language is expected to get the most out of this book.

Silo Roman

Author: Hugh Howey
Editor:
ISBN: 9783492055857
File Size: 50,34 MB
Format: PDF, Mobi
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Getting Started With Python For The Internet Of Things

Author: Tim Cox
Editor: Packt Publishing Ltd
ISBN: 1838553339
File Size: 58,24 MB
Format: PDF, ePub, Mobi
Read: 8989
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Build clever, collaborative, and powerful automation systems with the Raspberry Pi and Python. Key Features Create your own Pi-Rover or Pi-Hexipod robots Develop practical applications in Python using Raspberry Pi Build your own Jarvis, a highly advanced computerized AI Book Description This Learning Path takes you on a journey in the world of robotics and teaches you all that you can achieve with Raspberry Pi and Python. It teaches you to harness the power of Python with the Raspberry Pi 3 and the Raspberry Pi zero to build superlative automation systems that can transform your business. You will learn to create text classifiers, predict sentiment in words, and develop applications with the Tkinter library. Things will get more interesting when you build a human face detection and recognition system and a home automation system in Python, where different appliances are controlled using the Raspberry Pi. With such diverse robotics projects, you'll grasp the basics of robotics and its functions, and understand the integration of robotics with the IoT environment. By the end of this Learning Path, you will have covered everything from configuring a robotic controller, to creating a self-driven robotic vehicle using Python. Raspberry Pi 3 Cookbook for Python Programmers - Third Edition by Tim Cox, Dr. Steven Lawrence Fernandes Python Programming with Raspberry Pi by Sai Yamanoor, Srihari Yamanoor Python Robotics Projects by Prof. Diwakar Vaish What you will learn Build text classifiers and predict sentiment in words with the Tkinter library Develop human face detection and recognition systems Create a neural network module for optical character recognition Build a mobile robot using the Raspberry Pi as a controller Understand how to interface sensors, actuators, and LED displays work Apply machine learning techniques to your models Interface your robots with Bluetooth Who this book is for This Learning Path is specially designed for Python developers who want to take their skills to the next level by creating robots that can enhance people’s lives. Familiarity with Python and electronics will aid understanding the concepts in this Learning Path.

Amazon Ec2 Cookbook

Author: Sekhar Reddy
Editor: Packt Publishing Ltd
ISBN: 1785282255
File Size: 55,62 MB
Format: PDF, ePub, Mobi
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Over 40 hands-on recipes to develop and deploy real-world applications using Amazon EC2 About This Book Design and build applications using Amazon EC2 and a range of supporting AWS tools Find highly effective solutions to your AWS Cloud-based application development, deployment, and infrastructural issues A comprehensive set of recipes to implement your product's functional and non-functional requirements Who This Book Is For This book is targeted at Cloud-based developers who have prior exposure to AWS concepts and features. Some experience in building small applications and creating some proof-of-concept applications is required. What You Will Learn Select and configure the right EC2 instances Create, configure, and secure a Virtual Private Cloud Create an AWS CloudFormation template Use AWS Identity and Access Management to secure access to EC2 instances Configure auto-scaling groups using CloudWatch Choose and use the right data service such as SimpleDB and DynamoDB for your cloud applications Access key AWS services using client tools and AWS SDKs Deploy AWS applications using Docker containers In Detail Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides flexible and resizable compute capacity in the cloud. The main purpose of Amazon EC2 is to make web-scale cloud computing easier for the developers. It offers developers and companies the raw building blocks like load balancers, object stores and virtual machines running on general hardware (that is, Amazon runs a multitude of hardware components but presents them as a generic utility to its users) with accessible APIs in order to create scalable software products This book covers designing, developing, and deploying scalable, highly available, and secure applications on the AWS platform. By following the steps in the recipes, you will be able to effectively and systematically resolve issues related to development, deployment, and infrastructure for enterprise-grade cloud applications or products. This book starts with helping you choose and configure the right EC2 instances to meet your application-specific requirements. The book then moves on to creating a CloudFormation template and will teach you how to work with stacks. You will then be introduced to using IAM services to configure users, groups, roles, and multi-factor authentication. You will also learn how to connect AD to AWS IAM. Next, you will be using AWS data services and accessing other AWS services including Route 53, Amazon S3, and AWS SES (Amazon Simple Email Service). Finally, you will be deploying AWS applications using Docker containers. Style and approach This book contains a rich set of recipes that cover not only the full spectrum of real-world cloud application development using Amazon EC2, but also the services and security of the applications. The book contains easy-to-follow recipes with step-by-step instructions to leverage EC2 within your applications.

Reactive Systems Architecture

Author: Jan Machacek
Editor:
ISBN: 9781491980712
File Size: 58,18 MB
Format: PDF
Read: 7896
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Distributed systems have helped application development teams deal with failures, downtime, and poor scaling, but these systems bring technical challenges of their own. With this unique cookbook, system architects will get a detailed understanding of reactive systems, along with proven recipes for dealing with different architectural issues. Each self-contained chapter covers the architecture of an entire reactive system, and--since these systems share many of the same architectural issues--each chapter also focuses on a particular area, such as delivery semantics or monitoring & tracing, with detailed solutions for problems that commonly arise. Learn the architecture and implementation tips for an entire reactive microservices-based system in each chapter Understand the challenges of long-term running and evolution of your distributed system Explore different failure modes of distributed systems and the approaches to address them Learn about proper site reliability and production readiness

Splunk Enterprise Operational Intelligence Delivered

Author: Betsy Page Sigman
Editor: Packt Publishing Ltd
ISBN: 1787280918
File Size: 37,42 MB
Format: PDF, ePub, Docs
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Demystify Big Data and discover how to bring operational intelligence to your data to revolutionize your work About This Book Get maximum use out of your data with Splunk's exceptional analysis and visualization capabilities Analyze and understand your operational data skillfully using this end-to-end course Full coverage of high-level Splunk techniques such as advanced searches, manipulations, and visualization Who This Book Is For This course is for software developers who wish to use Splunk for operational intelligence to make sense of their machine data. The content in this course will appeal to individuals from all facets of business, IT, security, product, marketing, and many more What You Will Learn Install and configure the latest version of Splunk. Use Splunk to gather, analyze, and report data Create Dashboards and Visualizations that make data meaningful Model and accelerate data and perform pivot-based reporting Integrate advanced JavaScript charts and leverage Splunk's APIs Develop and Manage apps in Splunk Integrate Splunk with R and Tableau using SDKs In Detail Splunk is an extremely powerful tool for searching, exploring, and visualizing data of all types. Splunk is becoming increasingly popular, as more and more businesses, both large and small, discover its ease and usefulness. Analysts, managers, students, and others can quickly learn how to use the data from their systems, networks, web traffic, and social media to make attractive and informative reports. This course will teach everything right from installing and configuring Splunk. The first module is for anyone who wants to manage data with Splunk. You'll start with very basics of Splunk— installing Splunk— before then moving on to searching machine data with Splunk. You will gather data from different sources, isolate them by indexes, classify them into source types, and tag them with the essential fields. With more than 70 recipes on hand in the second module that demonstrate all of Splunk's features, not only will you find quick solutions to common problems, but you'll also learn a wide range of strategies and uncover new ideas that will make you rethink what operational intelligence means to you and your organization. Dive deep into Splunk to find the most efficient solution to your data problems in the third module. Create the robust Splunk solutions you need to make informed decisions in big data machine analytics. From visualizations to enterprise integration, this well-organized high level guide has everything you need for Splunk mastery. 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: Splunk Essentials - Second Edition Splunk Operational Intelligence Cookbook - Second Edition Advanced Splunk Style and approach Packed with several step by step tutorials and a wide range of techniques to take advantage of Splunk and its wide range of capabilities to deliver operational intelligence within your enterpise

Mastering Social Media Mining With R

Author: Sharan Kumar Ravindran
Editor: Packt Publishing Ltd
ISBN: 1784399671
File Size: 16,39 MB
Format: PDF
Read: 5758
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Extract valuable data from your social media sites and make better business decisions using R About This Book Explore the social media APIs in R to capture data and tame it Employ the machine learning capabilities of R to gain optimal business value A hands-on guide with real-world examples to help you take advantage of the vast opportunities that come with social media data Who This Book Is For If you have basic knowledge of R in terms of its libraries and are aware of different machine learning techniques, this book is for you. Those with experience in data analysis who are interested in mining social media data will find this book useful. What You Will Learn Access APIs of popular social media sites and extract data Perform sentiment analysis and identify trending topics Measure CTR performance for social media campaigns Implement exploratory data analysis and correlation analysis Build a logistic regression model to detect spam messages Construct clusters of pictures using the K-means algorithm and identify popular personalities and destinations Develop recommendation systems using Collaborative Filtering and the Apriori algorithm In Detail With an increase in the number of users on the web, the content generated has increased substantially, bringing in the need to gain insights into the untapped gold mine that is social media data. For computational statistics, R has an advantage over other languages in providing readily-available data extraction and transformation packages, making it easier to carry out your ETL tasks. Along with this, its data visualization packages help users get a better understanding of the underlying data distributions while its range of "standard" statistical packages simplify analysis of the data. This book will teach you how powerful business cases are solved by applying machine learning techniques on social media data. You will learn about important and recent developments in the field of social media, along with a few advanced topics such as Open Authorization (OAuth). Through practical examples, you will access data from R using APIs of various social media sites such as Twitter, Facebook, Instagram, GitHub, Foursquare, LinkedIn, Blogger, and other networks. We will provide you with detailed explanations on the implementation of various use cases using R programming. With this handy guide, you will be ready to embark on your journey as an independent social media analyst. Style and approach This easy-to-follow guide is packed with hands-on, step-by-step examples that will enable you to convert your real-world social media data into useful, practical information.