Build neural network models in text, vision and advanced analytics using PyTorch About This Book ? Learn PyTorch for implementing cutting-edge deep learning algorithms. ? Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; ? Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples; Who This Book Is For This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected. What You Will Learn ? Use PyTorch for GPU-accelerated tensor computations ? Build custom datasets and data loaders for images and test the models using torchvision and torchtext ? Build an image classifier by implementing CNN architectures using PyTorch ? Build systems that do text classification and language modeling using RNN, LSTM, and GRU ? Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning ? Learn how to mix multiple models for a powerful ensemble model ? Generate new images using GAN’s and generate artistic images using style transfer In Detail Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease. Style and approach An end-to-end guide that teaches you all about PyTorch and how to implement it in various scenarios.

Learn how to automate and manage your containers and reduce the overall operation burden on your system. About This Book ? Use containers to manage, scale and orchestrate apps in your organization ? Transform the latest concept of Kubernetes 1.10 into examples ? Expert techniques for orchestrating containers effectively Who This Book Is For This book is for system administrators, developers, DevOps engineers, or any stakeholder who wants to understand how Kubernetes works using a recipe-based approach. Basic knowledge of Kubernetes and Containers is required. What You Will Learn ? Build your own container cluster ? Deploy and manage highly scalable, containerized applications with Kubernetes ? Build high-availability Kubernetes clusters ? Build a continuous delivery pipeline for your application ? Track metrics and logs for every container running in your cluster ? Streamline the way you deploy and manage your applications with large-scale container orchestration In Detail Kubernetes is an open source orchestration platform to manage containers in a cluster environment. With Kubernetes, you can configure and deploy containerized applications easily. This book gives you a quick brush up on how Kubernetes works with containers, and an overview of main Kubernetes concepts, such as Pods, Deployments, Services and etc. This book explains how to create Kubernetes clusters and run applications with proper authentication and authorization configurations. With real-world recipes, you'll learn how to create high availability Kubernetes clusters on AWS, GCP and in on-premise datacenters with proper logging and monitoring setup. You'll also learn some useful tips about how to build a continuous delivery pipeline for your application. Upon completion of this book, you will be able to use Kubernetes in production and will have a better understanding of how to manage containers using Kubernetes. Style and approach This recipe-based book will teach you how to use Kubernetes in production and will help you discover various steps involved in managing your containers using Kubernetes

Be an adaptive thinker that leads the way to Artificial Intelligence About This Book ? AI-based examples to guide you in designing and implementing machine intelligence ? Develop your own method for future AI solutions ? Acquire advanced AI, machine learning, and deep learning design skills Who This Book Is For Artificial Intelligence by Example is a simple, explanatory, and descriptive guide for junior developers, experienced developers, technology consultants, and those interested in AI who want to understand the fundamentals of Artificial Intelligence and implement it practically by devising smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this book. What You Will Learn ? Use adaptive thinking to solve real-life AI case studies ? Rise beyond being a modern-day factory code worker ? Acquire advanced AI, machine learning, and deep learning designing skills ? Learn about cognitive NLP chatbots, quantum computing, and IoT and blockchain technology ? Understand future AI solutions and adapt quickly to them ? Develop out-of-the-box thinking to face any challenge the market presents In Detail Artificial Intelligence has the potential to replicate humans in every field. This book serves as a starting point for you to understand how AI is built, with the help of intriguing examples and case studies. Artificial Intelligence By Example will make you an adaptive thinker and help you apply concepts to real-life scenarios. Using some of the most interesting AI examples, right from a simple chess engine to a cognitive chatbot, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks. You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques. This comprehensive guide will be a starter kit for you to develop AI applications on your own. By the end of this book, will have understood the fundamentals of AI and worked through a number of case studies that will help you develop business vision. Style and approach An easy-to-follow step by step guide which will help you get to grips with real world application of Artificial Intelligence

Optimize the powerful techniques of Java 9 to boost your application's performance About This Book ? Tackle all kinds of performance-related issues and streamline your development ? Dive into the new features of Java 9 ? Implement highly efficient and reliable codes with the help of new APIs of Java ? Embedded with assessments that will help you revise the concepts you have learned in this book Who This Book Is For This book is targeted at developers who would like to build reliable and high-performance applications with Java. What You Will Learn ? Familiarize with modular development and its impact on performance ? Learn various string-related performance improvements, including compact string and modify string concatenation ? Explore various underlying compiler improvements, such as tiered attribution and Ahead-of-Time (AOT) compilation ? Learn security manager improvements ? Understand enhancements in graphics rasterizers ? Use of command-line tools to speed up application development ? Learn how to implement multithreading and reactive programming ? Build microservices in Java 9 ? Implement APIs to improve application code In Detail Java 9 which is one of the most popular application development languages. The latest released version Java 9 comes with a host of new features and new APIs with lots of ready to use components to build efficient and scalable applications. Streams, parallel and asynchronous processing, multithreading, JSON support, reactive programming, and microservices comprise the hallmark of modern programming and are now fully integrated into the JDK. This book focuses on providing quick, practical solutions to enhance your application's performance. You will explore the new features, APIs, and various tools added in Java 9 that help to speed up the development process. You will learn about jshell, Ahead-of-Time (AOT) compilation, and the basic threads related topics including sizing and synchronization. You will also explore various strategies for building microservices including container-less, self-contained, and in-container. This book is ideal for developers who would like to build reliable and high-performance applications with Java. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. Style and approach This book is a fast-paced guide that provides practical solutions to enhance your application’s performance. Note: This book is a blend of text and quizzes, all packaged up keeping your journey in mind. It includes content from the following Packt product: ? Java 9 High Performance by Mayur Ramgir and Nick Samoylov

Delve deep into various security aspects of AWS to build and maintain a secured environment About This Book ? Learn to secure your network, infrastructure, data, and applications in AWS cloud ? Use AWS managed security services to automate security ? Dive deep into various aspects such as the security model, compliance, access management and much more to build and maintain a secured environment ? Explore Cloud Adoption Framework (CAF) and its components ? Embedded with assessments that will help you revise the concepts you have learned in this book Who This Book Is For This book is for all IT professionals, system administrators, security analysts, solution architects, and chief information security officers who are responsible for securing workloads in AWS for their organizations. What You Will Learn ? Get familiar with VPC components, features, and benefits ? Learn to create and secure your private network in AWS ? Explore encryption and decryption fundamentals ? Understand monitoring, logging, and auditing in AWS ? Ensure data security in AWS ? Secure your web and mobile applications in AWS ? Learn security best practices for IAM, VPC, shared security responsibility model, and so on In Detail With organizations moving their workloads, applications, and infrastructure to the cloud at an unprecedented pace, security of all these resources has been a paradigm shift for all those who are responsible for security; experts, novices, and apprentices alike. This book focuses on using native AWS security features and managed AWS services to help you achieve continuous security. Starting with an introduction to Virtual Private Cloud (VPC) to secure your AWS VPC, you will quickly explore various components that make up VPC such as subnets, security groups, various gateways, and many more. You will also learn to protect data in the AWS platform for various AWS services by encrypting and decrypting data in AWS. You will also learn to secure web and mobile applications in AWS cloud. This book is ideal for all IT professionals, system administrators, security analysts, solution architects, and chief information security officers who are responsible for securing workloads in AWS for their organizations. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. Style and approach This book follows a practical approach delving into different aspects of AWS security. It focuses on using native AWS security features and managed AWS services to help you achieve continuous security. Note: This book is a blend of text and quizzes, all packaged up keeping your journey in mind. It includes content from the following Packt product: ? Mastering AWS Security by Albert Anthony

Unlock the power of Spring Boot to build and deploy production-ready microservices About This Book ? Get to know the advanced features of Spring Boot in order to develop and monitor applications ? Use Spring cloud to deploy and manage microservices on the cloud ? Look at embedded servers and deploy a test application to a PaaS Cloud platform ? Embedded with assessments that will help you revise the concepts you have learned in this book Who This Book Is For This book is aimed at Java developers who knows the basics of Spring programming and want to build microservices with Spring Boot. What You Will Learn ? Use Spring Initializr to create a basic spring project ? Build a basic microservice with Spring Boot ? Implement caching and exception handling ? Secure your microservice with Spring security and OAuth2 ? Deploy microservices using self-contained HTTP server ? Monitor your microservices with Spring Boot actuator ? Learn to develop more effectively with developer tools In Detail Microservices helps in decomposing applications into small services and move away from a single monolithic artifact. It helps in building systems that are scalable, flexible, and high resilient. Spring Boot helps in building REST-oriented, production-grade microservices. This book is a quick learning guide on how to build, monitor, and deploy microservices with Spring Boot. You'll be first familiarized with Spring Boot before delving into building microservices. You will learn how to document your microservice with the help of Spring REST docs and Swagger documentation. You will then learn how to secure your microservice with Spring Security and OAuth2. You will deploy your app using a self-contained HTTP server and also learn to monitor a microservice with the help of Spring Boot actuator. This book is ideal for Java developers who knows the basics of Spring programming and want to build microservices with Spring Boot. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. Style and approach This book follows a practical approach to teach you how to build, monitor, and deploy microservices with Spring Boot. Note: This book is a blend of text and quizzes, all packaged up keeping your journey in mind. It includes content from the following Packt product: ? Mastering Spring 5.0 by Ranga Rao Karanam

Become a better web programmer by writing efficient and modular code using ES6 and ES8 About This Book ? Learn to write asynchronous code and improve the readability of your web applications ? Explore advanced concepts such as closures, Proxy, generators, Promise, async functions, and Atomics ? Use different design patterns to create structures to solve common organizational and processing issues Who This Book Is For If you’re a web developer with a basic understanding of JavaScript and wish to learn the latest features of ECMAScript for developing efficient web applications, this book is for you. What You Will Learn ? Organize JavaScript programs across multiple files, using ES modules ? Create and work with promises using the Promise object and methods ? Compose async functions to propagate and handle errors ? Solve organizational and processing issues with structures using design patterns ? Use classes to encapsulate and share behavior ? Orchestrate parallel programs using WebWorkers, SharedMemory, and Atomics ? Use and extend Map, Set, and Symbol to work with user-defined classes and simulate data types ? Explore new array methods to avoid looping with arrays and other collections In Detail ECMAScript Cookbook follows a modular approach with independent recipes covering different feature sets and specifications of ECMAScript to help you become an efficient programmer. This book starts off with organizing your JavaScript applications as well as delivering those applications to modem and legacy systems. You will get acquainted with features of ECMAScript 8 such as async, SharedArrayBuffers, and Atomic operations that enhance asynchronous and parallel operations. In addition to this, this book will introduce you to SharedArrayBuffers, which allow web workers to share data directly, and Atomic operations, which help coordinate behavior across the threads. You will also work with OOP and Collections, followed by new functions and methods on the built-in Object and Array types that make common operations more manageable and less error-prone. You will then see how to easily build more sophisticated and expressive program structures with classes and inheritance. In the end, we will cover Sets, Maps, and Symbols, which are the new types introduced in ECMAScript 6 to add new behaviors and allow you to create simple and powerful modules. By the end of the book, you will be able to produce more efficient, expressive, and simpler programs using the new features of ECMAScript. Style and approach This book will follow a modular approach covering independent recipes on different features of ECMAScript throughout the book.

A comprehensive guide to design, build and execute effective Big Data strategies using Hadoop About This Book ? Get an in-depth view of the Apache Hadoop ecosystem and an overview of the architectural patterns pertaining to the popular Big Data platform ? Conquer different data processing and analytics challenges using a multitude of tools such as Apache Spark, Elasticsearch, Tableau and more ? A comprehensive, step-by-step guide that will teach you everything you need to know, to be an expert Hadoop Architect Who This Book Is For This book is for Big Data professionals who want to fast-track their career in the Hadoop industry and become an expert Big Data architect. Project managers and mainframe professionals looking forward to build a career in Big Data Hadoop will also find this book to be useful. Some understanding of Hadoop is required to get the best out of this book. What You Will Learn ? Build an efficient enterprise Big Data strategy centered around Apache Hadoop ? Gain a thorough understanding of using Hadoop with various Big Data frameworks such as Apache Spark, Elasticsearch and more ? Set up and deploy your Big Data environment on premises or on the cloud with Apache Ambari ? Design effective streaming data pipelines and build your own enterprise search solutions ? Utilize the historical data to build your analytics solutions and visualize them using popular tools such as Apache Superset ? Plan, set up and administer your Hadoop cluster efficiently In Detail The complex structure of data these days requires sophisticated solutions for data transformation, to make the information more accessible to the users.This book empowers you to build such solutions with relative ease with the help of Apache Hadoop, along with a host of other Big Data tools. This book will give you a complete understanding of the data lifecycle management with Hadoop, followed by modeling of structured and unstructured data in Hadoop. It will also show you how to design real-time streaming pipelines by leveraging tools such as Apache Spark, and build efficient enterprise search solutions using Elasticsearch. You will learn to build enterprise-grade analytics solutions on Hadoop, and how to visualize your data using tools such as Apache Superset. This book also covers techniques for deploying your Big Data solutions on the cloud Apache Ambari, as well as expert techniques for managing and administering your Hadoop cluster. By the end of this book, you will have all the knowledge you need to build expert Big Data systems. Style and approach Comprehensive guide with a perfect blend of theory, examples and implementation of real-world use-cases

This fast-paced guide will quickly enhance your skills to develop a highly scalable Cloud environment About This Book ? Efficiently build a highly scalable and reliable cloud environment for your applications with AWS ? Leverage the various AWS components and services to build a secure, reliable, and robust environment to host your applications on ? This quick-start guide will quickly enhance your skills to develop highly scalable services Who This Book Is For This book is for IT professionals and system administrators looking to design, deploy, and manage your applications and services on the AWS cloud platform. It’s also ideal for developers looking to build highly scalable cloud-based services. A basic understanding of AWS would be beneficial. What You Will Learn ? Find out about IAM to access AWS services securely ? Explore EC2 (virtual server) and scale up/down your application based on heavy traffic ? Learn about unlimited data storage service S3 and host a static website within minutes ? Get to grips with Relational Databases and NoSQL databases under the AWS ecosystem ? Understand the caching mechanism ? Get to know about notifications service and monitor AWS services ? Secure and troubleshoot your AWS architecture In Detail AWS is at the forefront of Cloud Computing today. Businesses are adopting AWS Cloud because of its reliability, versatility, and flexible design. The main focus of this book is teaching you how to build and manage highly reliable and scalable applications and services on AWS. It will provide you with all the necessary skills to design, deploy, and manage your applications and services on the AWS cloud platform. We’ll start by exploring Amazon S3, EC2, and so on to get you well-versed with core Amazon services. Moving on, we’ll teach you how to design and deploy highly scalable and optimized workloads. You’ll also discover easy-to-follow, hands-on steps, tips, and recommendations throughout the book and get to know essential security and troubleshooting concepts. By the end of the book, you’ll be able to create a highly secure, fault tolerant, and scalable environment for your applications to run on. Style and approach This book is all about fast and intensive learning. That means we don’t waste time helping you get started. The new features provided by AWS resources are being covered with highly-effective examples to develop new things, demonstrating different ways to create and use the AWS resources efficiently.

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow v1.7. About This Book ? Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow v1.7 ? Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide ? Gain real-world contextualization through some deep learning problems concerning research and application Who This Book Is For The book is for people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus. What You Will Learn ? Apply deep machine intelligence and GPU computing with TensorFlow v1.7 ? Access public datasets and use TensorFlow to load, process, and transform the data ? Discover how to use the high-level TensorFlow API to build more powerful applications ? Use deep learning for scalable object detection and mobile computing ? Train machines quickly to learn from data by exploring reinforcement learning techniques ? Explore active areas of deep learning research and applications In Detail Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow v1.7, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects. Style and approach This step-by-step guide explores common, and not so common, deep neural networks, and shows how they can be exploited in the real world with complex raw data. Benefit from practical examples, and learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing.

Build rich and collaborative applications using client-side code with React, Redux, and Firebase About This Book ? A practical guide covering the full stack for web development with React 16 and Firebase ? Leverage the power of Firebase Cloud Storage, messaging, functions, OAuth, and database security to develop serverless web applications. ? Develop high-performance applications without the hassle of setting up complex web infrastructure. Who This Book Is For This book is for JavaScript developers who have some previous knowledge of React and want to develop serverless, full-stack applications but without the hassle of setting up a complex infrastructure. What You Will Learn ? Install powerful React.js and Firebase tools to make development much more efficient ? Create React components with Firebase to save and retrieve the data in real-time ? Use Firebase Authentication to make your React user interface secure ? Develop React and Firebase applications with Redux integration ? Firebase database security rules ? Firebase Cloud Storage Integration to upload and store data on the cloud ? Create a complete real-time application with React and firebase ? Using Firebase Cloud messaging and Cloud functions with React ? Firebase Cloud Storage integration with React In Detail ReactJS is a wonderful framework for UI development. Firebase as a backend with React is a great choice as it is easy, powerful, and provides great developer experience. It removes a lot of boilerplate code from your app and allows you to focus on your app to get it out quickly to users. Firebase with React is also a good choice for Most Viable Product (MVP) development. This book provides more practical insights rather than just theoretical concepts and includes basic to advanced examples – from hello world to a real-time seat booking app and Helpdesk application This book will cover the essentials of Firebase and React.js and will take you on a fast-paced journey through building real-time applications with Firebase features such as Cloud Storage, Cloud Function, Hosting and the Realtime Database. We will learn how to secure our application by using Firebase authentication and database security rules. We will leverage the power of Redux to organize data in the front-end, since Redux attempts to make state mutations predictable by imposing certain restrictions on how and when updates can happen. Towards the end of the book you will have improved your React skills by realizing the potential of Firebase to create real-time serverless web applications. Style and approach Practical insights rather than just theoretical concepts while including basic to advanced examples – from hello world to a real-time seat booking app and Helpdesk application.

Combine the power of analytics and cloud computing for faster and efficient insights About This Book ? Master the concept of analytics on the cloud: and how organizations are using it ? Learn the design considerations and while applying a cloud analytics solution ? Design an end-to-end analytics pipeline on the cloud Who This Book Is For This book is targeted at CIOs, CTOs, and even analytics professionals looking for various alternatives to implement their analytics pipeline on the cloud. Data professionals looking to get started with cloud-based analytics will also find this book useful. Some basic exposure to cloud platforms such as GCP will be helpful, but not mandatory. What You Will Learn ? Explore the basics of cloud analytics and the major cloud solutions ? Learn how organizations are using cloud analytics to improve the ROI ? Explore the design considerations while adopting cloud services ? Work with the ingestion and storage tools of GCP such as Cloud Pub/Sub ? Process your data with tools such as Cloud Dataproc, BigQuery, etc ? Over 70 GCP tools to build an analytics engine for cloud analytics ? Implement machine learning and other AI techniques on GCP In Detail With the ongoing data explosion, more and more organizations all over the world are slowly migrating their infrastructure to the cloud. These cloud platforms also provide their distinct analytics services to help you get faster insights from your data. This book will give you an introduction to the concept of analytics on the cloud, and the different cloud services popularly used for processing and analyzing data. If you’re planning to adopt the cloud analytics model for your business, this book will help you understand the design and business considerations to be kept in mind, and choose the best tools and alternatives for analytics, based on your requirements. The chapters in this book will take you through the 70+ services available in Google Cloud Platform and their implementation for practical purposes. From ingestion to processing your data, this book contains best practices on building an end-to-end analytics pipeline on the cloud by leveraging popular concepts such as machine learning and deep learning. By the end of this book, you will have a better understanding of cloud analytics as a concept as well as a practical know-how of its implementation Style and approach Comprehensive guide with a perfect blend of theory, examples, and implementation of real-world use-cases

Your one stop guide to automating infrastructure security using DevOps and DevSecOps Key Features * Secure and automate techniques to protect web, mobile or cloud services * Automate secure code inspection in C++, Java, Python, and JavaScript * Integrate security testing with automation frameworks like fuzz, BDD, Selenium and Robot Framework Book Description Security automation is the automatic handling of software security assessments tasks. This book helps you to build your security automation framework to scan for vulnerabilities without human intervention. This book will teach you to adopt security automation techniques to continuously improve your entire software development and security testing. You will learn to use open source tools and techniques to integrate security testing tools directly into your CI/CD framework. With this book, you will see how to implement security inspection at every layer, such as secure code inspection, fuzz testing, Rest API, privacy, infrastructure security, and web UI testing. With the help of practical examples, this book will teach you to implement the combination of automation and Security in DevOps. You will learn about the integration of security testing results for an overall security status for projects. By the end of this book, you will be confident implementing automation security in all layers of your software development stages and will be able to build your own in-house security automation platform throughout your mobile and cloud releases. What you will learn * Automate secure code inspection with open source tools and effective secure code scanning suggestions * Apply security testing tools and automation frameworks to identify security vulnerabilities in web, mobile and cloud services * Integrate security testing tools such as OWASP ZAP, NMAP, SSLyze, SQLMap, and OpenSCAP * Implement automation testing techniques with Selenium, JMeter, Robot Framework, Gauntlt, BDD, DDT, and Python unittest * Execute security testing of a Rest API Implement web application security with open source tools and script templates for CI/CD integration * Integrate various types of security testing tool results from a single project into one dashboard Who this book is for The book is for software developers, architects, testers and QA engineers who are looking to leverage automated security testing techniques.

Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Key Features * Discover neural network architectures (like CNN and LSTM) that are driving recent advancements in AI * Build expert neural networks in Python using popular libraries such as Keras * Includes projects such as object detection, face identification, sentiment analysis, and more Book Description Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio. What you will learn * Learn various neural network architectures and its advancements in AI * Master deep learning in Python by building and training neural network * Master neural networks for regression and classification * Discover convolutional neural networks for image recognition * Learn sentiment analysis on textual data using Long Short-Term Memory * Build and train a highly accurate facial recognition security system Who this book is for This book is a perfect match for data scientists, machine learning engineers, and deep learning enthusiasts who wish to create practical neural network projects in Python. Readers should already have some basic knowledge of machine learning and neural networks.

Create a real-world application in Go and explore various frameworks and methodologies for full-stack development Key Features * Organize your isomorphic codebase to enhance the maintainability of your application * Build web APIs and middleware in the Go language by making use of the popular Gin framework * Implement real-time web application functionality with WebSockets Book Description The Go programming language has been rapidly adopted by developers for building web applications. With its impressive performance and ease of development, Go enjoys the support of a wide variety of open source frameworks, for building scalable and high-performant web services and apps. Hands-On Full Stack Development with Go is a comprehensive guide that covers all aspects of full stack development with Go. This clearly written, example-rich book begins with a practical exposure to Go development and moves on to build a frontend with the popular React framework. From there, you will build RESTful web APIs utilizing the Gin framework. After that, we will dive deeper into important software backend concepts, such as connecting to the database via an ORM, designing routes for your services, securing your services, and even charging credit cards via the popular Stripe API. We will also cover how to test, and benchmark your applications efficiently in a production environment. In the concluding chapters, we will cover isomorphic developments in pure Go by learning about GopherJS. As you progress through the book, you'll gradually build a musical instrument online store application from scratch. By the end of the book, you will be confident in taking on full stack web applications in Go. What you will learn * Understand Go programming by building a real-world application * Learn the React framework to develop a frontend for your application * Understand isomorphic web development utilizing the GopherJS framework * Explore methods to write RESTful web APIs in Go using the Gin framework * Learn practical topics such as ORM layers, secure communications, and Stripe's API * Learn methods to benchmark and test web APIs in Go Who this book is for Hands-On Full Stack Development with Go will appeal to developers who are looking to start building amazing full stack web applications in Go. Basic knowhow of Go language and JavaScript is expected. The book targets web developers who are looking to move to the Go language.

Learn how to build complete machine learning systems with IBM Cloud and Watson Machine learning services Key Features * Implement data science and machine learning techniques to draw insights from real-world data * Understand what IBM Cloud platform can help you to implement cognitive insights within applications * Understand the role of data representation and feature extraction in any machine learning system Book Description IBM Cloud is a collection of cloud computing services for data analytics using machine learning and artificial intelligence (AI). This book is a complete guide to help you become well versed with machine learning on the IBM Cloud using Python. Hands-On Machine Learning with IBM Watson starts with supervised and unsupervised machine learning concepts, in addition to providing you with an overview of IBM Cloud and Watson Machine Learning. You'll gain insights into running various techniques, such as K-means clustering, K-nearest neighbor (KNN), and time series prediction in IBM Cloud with real-world examples. The book will then help you delve into creating a Spark pipeline in Watson Studio. You will also be guided through deep learning and neural network principles on the IBM Cloud using TensorFlow. With the help of NLP techniques, you can then brush up on building a chatbot. In later chapters, you will cover three powerful case studies, including the facial expression classification platform, the automated classification of lithofacies, and the multi-biometric identity authentication platform, helping you to become well versed with these methodologies. By the end of this book, you will be ready to build efficient machine learning solutions on the IBM Cloud and draw insights from the data at hand using real-world examples. What you will learn * Understand key characteristics of IBM machine learning services * Run supervised and unsupervised techniques in the cloud * Understand how to create a Spark pipeline in Watson Studio * Implement deep learning and neural networks on the IBM Cloud with TensorFlow * Create a complete, cloud-based facial expression classification solution * Use biometric traits to build a cloud-based human identification system Who this book is for This beginner-level book is for data scientists and machine learning engineers who want to get started with IBM Cloud and its machine learning services using practical examples. Basic knowledge of Python and some understanding of machine learning will be useful.

Your one-stop guide to learning and implementing artificial neural networks with Keras effectively Key Features * Design and create neural network architectures on different domains using Keras * Integrate neural network models in your applications using this highly practical guide * Get ready for the future of neural networks through transfer learning and predicting multi network models Book Description Neural networks are used to solve a wide range of problems in different areas of AI and deep learning. Hands-On Neural Networks with Keras will start with teaching you about the core concepts of neural networks. You will delve into combining different neural network models and work with real-world use cases, including computer vision, natural language understanding, synthetic data generation, and many more. Moving on, you will become well versed with convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, autoencoders, and generative adversarial networks (GANs) using real-world training datasets. We will examine how to use CNNs for image recognition, how to use reinforcement learning agents, and many more. We will dive into the specific architectures of various networks and then implement each of them in a hands-on manner using industry-grade frameworks. By the end of this book, you will be highly familiar with all prominent deep learning models and frameworks, and the options you have when applying deep learning to real-world scenarios and embedding artificial intelligence as the core fabric of your organization. What you will learn * Understand the fundamental nature and workflow of predictive data modeling * Explore how different types of visual and linguistic signals are processed by neural networks * Dive into the mathematical and statistical ideas behind how networks learn from data * Design and implement various neural networks such as CNNs, LSTMs, and GANs * Use different architectures to tackle cognitive tasks and embed intelligence in systems * Learn how to generate synthetic data and use augmentation strategies to improve your models * Stay on top of the latest academic and commercial developments in the field of AI Who this book is for This book is for machine learning practitioners, deep learning researchers and AI enthusiasts who are looking to get well versed with different neural network architecture using Keras. Working knowledge of Python programming language is mandatory.

Write efficient, reusable scripts to build custom characters, game environments, and control enemy AI Key Features * Build captivating multiplayer games using Unreal Engine and C++ * Incorporate existing C++ libraries into your game to add extra functionality such as hardware integration * Practical solutions for memory management, error handling, inputs, and collision for your game codebase Book Description Unreal Engine 4 (UE4) is a popular and award-winning game engine that powers some of the most popular games. A truly powerful tool for game development, there has never been a better time to use it for both commercial and independent projects. With more than 100 recipes, this book shows how to unleash the power of C++ while developing games with Unreal Engine. This book takes you on a journey to jumpstart your C++ and UE4 development skills. You will start off by setting up UE4 for C++ development and learn how to work with Visual Studio, a popular code editor. You will learn how to create C++ classes and structs the Unreal way. This will be followed by exploring memory management, smart pointers, and debugging your code. You will then learn how to make your own Actors and Components through code and how to handle input and collision events. You will also get exposure to many elements of game development including creating user interfaces, artificial intelligence, and writing code with networked play in mind. You will also learn how to add on to the Unreal Editor itself. With a range of task-oriented recipes, this book provides actionable information about writing code for games with UE4 using C++. By the end of the book, you will be empowered to become a top-notch developer with UE4 using C++ as your scripting language! What you will learn * Create C++ classes and structs that integrate well with UE4 and the Blueprints editor * Discover how to work with various APIs that Unreal Engine already contains * Utilize advanced concepts such as events, delegates, and interfaces in your UE4 projects * Build user interfaces using Canvas and UMG through C++ * Extend the Unreal Editor by creating custom windows and editors * Implement AI tasks and services using C++, Blackboard, and Behavior Trees * Write C++ code with networking in mind and replicate properties and functions Who this book is for If you are really passionate game developer looking for solutions to common scripting problems, then this is the book for you. Understanding of the fundamentals of game design and C++ is expected to get the most from this book.

Implement defensive techniques in your ecosystem successfully with Python Key Features * Identify and expose vulnerabilities in your infrastructure with Python * Learn custom exploit development . * Make robust and powerful cybersecurity tools with Python Book Description With the current technological and infrastructural shift, penetration testing is no longer a process-oriented activity. Modern-day penetration testing demands lots of automation and innovation; the only language that dominates all its peers is Python. Given the huge number of tools written in Python, and its popularity in the penetration testing space, this language has always been the first choice for penetration testers. Hands-On Penetration Testing with Python walks you through advanced Python programming constructs. Once you are familiar with the core concepts, you’ll explore the advanced uses of Python in the domain of penetration testing and optimization. You’ll then move on to understanding how Python, data science, and the cybersecurity ecosystem communicate with one another. In the concluding chapters, you’ll study exploit development, reverse engineering, and cybersecurity use cases that can be automated with Python. By the end of this book, you’ll have acquired adequate skills to leverage Python as a helpful tool to pentest and secure infrastructure, while also creating your own custom exploits. What you will learn * Get to grips with Custom vulnerability scanner development * Familiarize yourself with web application scanning automation and exploit development * Walk through day-to-day cybersecurity scenarios that can be automated with Python * Discover enterprise-or organization-specific use cases and threat-hunting automation * Understand reverse engineering, fuzzing, buffer overflows , key-logger development, and exploit development for buffer overflows. * Understand web scraping in Python and use it for processing web responses * Explore Security Operations Centre (SOC) use cases * Get to understand Data Science, Python, and cybersecurity all under one hood Who this book is for If you are a security consultant , developer or a cyber security enthusiast with little or no knowledge of Python and want in-depth insight into how the pen-testing ecosystem and python combine to create offensive tools , exploits , automate cyber security use-cases and much more then this book is for you. Hands-On Penetration Testing with Python guides you through the advanced uses of Python for cybersecurity and pen-testing, helping you to better understand security loopholes within your infrastructure .

Get ready for the Windows 10: 70-698 exam and configure Windows to manage data recovery Key Features * Implement Windows 10 operational and administrative tasks * Configure devices, remote management settings, advanced management tools, and device drivers * Comprehensive guide to help you work efficiently in Windows 10 Book Description The Installing and Configuring Windows 10: 70-698 Exam Guide is designed to confirm what you already know, while also updating your knowledge of Windows 10. With its easy-to-follow guidance, you will quickly learn the user interface and discover steps to work efficiently in Windows 10 to rule out delays and obstacles. This book begins by covering various ways of installing Windows 10, followed by instructions on post-installation tasks. You will learn about the deployment of Windows 10 in Enterprise and also see how to configure networking in Windows 10. You’ll understand how to leverage Disk Management and Windows PowerShell to configure disks, volumes, and file system options. As you progress through the chapters, you will be able to set up remote management in Windows 10 and learn more about Windows update usage, behavior, and settings. You will also gain insights that will help you monitor and manage data recovery and explore how to configure authentication, authorization, and advanced management tools in Windows 10. By the end of this book, you will be equipped with enough knowledge to take the 70-698 exam and explore different study methods to improve your chances of passing the exam with ease. What you will learn * Discover various ways of installing Windows 10 * Understand how to configure devices and device drivers * Configure and support IPv4 and IPv6 network settings * Troubleshoot storage and removable device issues * Get to grips with data access and usage * Explore the advanced management tools available in Windows 10 Who this book is for This book is for IT professionals who perform installation, configuration, general local management and maintenance of Windows 10 core services and are preparing to clear the Windows 10: 70-698 exam

Implement machine learning and deep learning methodologies to build smart, cognitive AI projects using Python Key Features * A go-to guide to help you master AI algorithms and concepts * 8 real-world projects tackling different challenges in healthcare, e-commerce, and surveillance * Use TensorFlow, Keras, and other Python libraries to implement smart AI applications Book Description This book will be a perfect companion if you want to build insightful projects from leading AI domains using Python. The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning techniques. You will assimilate various neural network architectures such as CNN, RNN, LSTM, to solve critical new world challenges. You will learn to train a model to detect diabetic retinopathy conditions in the human eye and create an intelligent system for performing a video-to-text translation. You will use the transfer learning technique in the healthcare domain and implement style transfer using GANs. Later you will learn to build AI-based recommendation systems, a mobile app for sentiment analysis and a powerful chatbot for carrying customer services. You will implement AI techniques in the cybersecurity domain to generate Captchas. Later you will train and build autonomous vehicles to self-drive using reinforcement learning. You will be using libraries from the Python ecosystem such as TensorFlow, Keras and more to bring the core aspects of machine learning, deep learning, and AI. By the end of this book, you will be skilled to build your own smart models for tackling any kind of AI problems without any hassle. What you will learn * Build an intelligent machine translation system using seq-2-seq neural translation machines * Create AI applications using GAN and deploy smart mobile apps using TensorFlow * Translate videos into text using CNN and RNN * Implement smart AI Chatbots, and integrate and extend them in several domains * Create smart reinforcement, learning-based applications using Q-Learning * Break and generate CAPTCHA using Deep Learning and Adversarial Learning Who this book is for This book is intended for data scientists, machine learning professionals, and deep learning practitioners who are ready to extend their knowledge and potential in AI. If you want to build real-life smart systems to play a crucial role in every complex domain, then this book is what you need. Knowledge of Python programming and a familiarity with basic machine learning and deep learning concepts are expected to help you get the most out of the book