Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering how you can use deep learning models in your software applications with Microsoft Cognitive Toolkit Key Features * Understand the fundamentals of Microsoft Cognitive Toolkit and set up the development environment * Train different types of neural networks using Cognitive Toolkit and deploy it to production * Evaluate the performance of your models and improve your deep learning skills Book Description Cognitive Toolkit is a very popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks, such as convolutional and recurrent neural networks. This book will help you understand the basics of deep learning. You will learn how to use Microsoft Cognitive Toolkit to build deep learning models and discover what makes this framework unique so that you know when to use it. This book will be a quick, no-nonsense introduction to the library and will teach you how to train different types of neural networks, such as convolutional neural networks, recurrent neural networks, autoencoders, and more, using Cognitive Toolkit. Then we will look at two scenarios in which deep learning can be used to enhance human capabilities. The book will also demonstrate how to evaluate your models' performance to ensure it trains and runs smoothly and gives you the most accurate results. Finally, you will get a short overview of how Cognitive Toolkit fits in to a DevOps environment What you will learn * Set up your deep learning environment for the Cognitive Toolkit on Windows and Linux * Pre-process and feed your data into neural networks * Use neural networks to make effcient predictions and recommendations * Train and deploy effcient neural networks such as CNN and RNN * Detect problems in your neural network using TensorBoard * Integrate Cognitive Toolkit with Azure ML Services for effective deep learning Who this book is for Data Scientists, Machine learning developers, AI developers who wish to train and deploy effective deep learning models using Microsoft CNTK will find this book to be useful. Readers need to have experience in Python or similar object-oriented language like C# or Java.

Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games Key Features * Apply the power of deep learning to complex reasoning tasks by building a Game AI * Exploit the most recent developments in machine learning and AI for building smart games * Implement deep learning models and neural networks with Python Book Description The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments. As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning. What you will learn * Learn the foundations of neural networks and deep learning. * Use advanced neural network architectures in applications to create music, textures, self driving cars and chatbots. * Understand the basics of reinforcement and DRL and how to apply it to solve a variety of problems. * Working with Unity ML-Agents toolkit and how to install, setup and run the kit. * Understand core concepts of DRL and the differences between discrete and continuous action environments. * Use several advanced forms of learning in various scenarios from developing agents to testing games. Who this book is for This books is for game developers who wish to create highly interactive games by leveraging the power of machine and deep learning. No prior knowledge of machine learning, deep learning or neural networks is required this book will teach those concepts from scratch. A good understanding of Python is required.

Understand the fundamentals of C# programming and get started with coding from ground up in an engaging and practical manner Key Features * Beginner's guide to getting started with software development concepts from a macro level * Leverage the power of the latest C# in solving the complex programming problems * Learn to script and customize your 3D games and implement animation techniques to make them engaging Book Description Learning to program in today’s technical landscape can be a daunting task, especially when faced with the sheer number of languages you have to choose from. Luckily, Learning C# with Unity 2019 removes the guesswork and starts you off on the path to becoming a confident, and competent, programmer using game development with Unity. You’ll start off small by learning the building blocks of programming, from variables, methods, and conditional statements to classes and object-oriented systems. After you have the basics under your belt you’ll explore the Unity interface, creating C# scripts, and translating your newfound knowledge into simple game mechanics. Throughout this journey, you’ll get hands-on experience with programming best practices and macro-level topics such as manager classes and flexible application architecture. By the end of the book, you’ll be familiar with intermediate C# topics like generics, delegates, and events, setting you up to take on projects of your own. What you will learn * Understand programming fundamentals with practice examples in C# * Explore the interface and features of Unity 2019 * Learn C# programming syntax from scratch * Create a game design document and prototype level * Explore intermediate programming topics and best practices * Implement game mechanics, interactions, and UI elements with C# Who this book is for The book caters to developers and programmers who want to get started with C# programming in a fun and engaging manner. Anyone who wants to build games and script in C# language and Unity can take this book up. No prior programming or Unity experience is required.

Effectively implement and administer business solutions on any scale in a cost-effective way to have a competitive advantage using Gsuite Key Features * Enhance administration with Admin console and Google Apps Script * Prepare for the G suite certification using the concepts in the book * Learn how to use reports to monitor, troubleshoot and optimize G Suite Book Description Hands-On G Suite for Administrators is a comprehensive hands-on guide to G Suite Administration that will prepare you with all you need to know to become a certified G Suite Administrator, ready to handle all the business scales, from a small office to a large enterprise. You will start by learning the main features, tools, and services from G Suite for Business and then, you will explore all it has to offer and the best practices, so you can make the most out of it. We will explore G Suite tools in depth so you and your team get everything you need -combination of tools, settings and practices- to succeed in an intuitive, safe and collaborative way. While learning G Suite tools you will also learn how to use Google Sites and App Maker, to create from your corporate site to internal tools, live reports that seamlessly integrate with live documents, and advanced Google Services. Finally, you will learn how to set up, analyze and enforce Security, Privacy for your business and how to efficiently troubleshoot a wide variety of issues. What you will learn * Setting up G Suite for the business account * Work with the advanced setup of additional business domains and administrate users in multiple * Explore Guite's extensive set of features to cover your team’s creation and collaboration needs * Setup, manage and analyze your security to prevent, find or fix any security problem in G Suite * Manage Mobile devices and integrate with third-party apps * Create cloud documents, working alone or collaborating in real time Who this book is for System administrators, cloud administrators, business professionals, and aspirants of G Suite admin certificate wanting to master implementing G Suite tools for various admin tasks and effectively implement the G Suite administration for business

Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more. Key Features * Use R 3.5 to implement real-world examples in machine learning * Implement key machine learning algorithms to understand the working mechanism of smart models * Create end-to-end machine learning pipelines using modern libraries from the R ecosystem Book Description Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline. From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling. By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R. What you will learn * Introduce yourself to the basics of machine learning with R 3.5 * Get to grips with R techniques for cleaning and preparing your data for analysis and visualize your results * Learn to build predictive models with the help of various machine learning techniques * Use R to visualize data spread across multiple dimensions and extract useful features * Use interactive data analysis with R to get insights into data * Implement supervised and unsupervised learning, and NLP using R libraries Who this book is for This book is for graduate students, aspiring data scientists, and data analysts who wish to enter the field of machine learning and are looking to implement machine learning techniques and methodologies from scratch using R 3.5. A working knowledge of the R programming language is expected.

Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python. Key Features * Get a hands-on, fast-paced introduction to the Python data science stack * Explore ways to create useful metrics and statistics from large datasets * Create detailed analysis reports with real-world data Book Description Processing big data in real time is challenging due to scalability, information inconsistency, and fault tolerance. Big Data Analysis with Python teaches you how to use tools that can control this data avalanche for you. With this book, you'll learn practical techniques to aggregate data into useful dimensions for posterior analysis, extract statistical measurements, and transform datasets into features for other systems. The book begins with an introduction to data manipulation in Python using pandas. You'll then get familiar with statistical analysis and plotting techniques. With multiple hands-on activities in store, you'll be able to analyze data that is distributed on several computers by using Dask. As you progress, you'll study how to aggregate data for plots when the entire data cannot be accommodated in memory. You'll also explore Hadoop (HDFS and YARN), which will help you tackle larger datasets. The book also covers Spark and explains how it interacts with other tools. By the end of this book, you'll be able to bootstrap your own Python environment, process large files, and manipulate data to generate statistics, metrics, and graphs. What you will learn * Use Python to read and transform data into different formats * Generate basic statistics and metrics using data on disk * Work with computing tasks distributed over a cluster * Convert data from various sources into storage or querying formats * Prepare data for statistical analysis, visualization, and machine learning * Present data in the form of effective visuals Who this book is for Big Data Analysis with Python is designed for Python developers, data analysts, and data scientists who want to get hands-on with methods to control data and transform it into impactful insights. Basic knowledge of statistical measurements and relational databases will help you to understand various concepts explained in this book.

Create responsive eLearning content, including quizzes, demonstrations, simulations and Virtual Reality projects that fit on any device with Adobe Captivate 2019 Key Features * Build responsive, interactive and highly engaging eLearning content with Adobe Captivate 2019 * Build Virtual Reality eLearning experiences with Adobe Captivate 2019 * Assess your student knowledge with interactive and random quizzes * Seamlessly integrate your eLearning content with any SCORM or xAPI compliant LMS Book Description Adobe Captivate is used to create highly engaging, interactive, and responsive eLearning content. This book takes you through the production of a few pieces of eLearning content, covering all the project types and workflows of Adobe Captivate. First, you will learn how to create a typical interactive Captivate project. This will give you the opportunity to review all Captivate objects and uncover the application's main tools. Then, you will use the built-in capture engine of Captivate to create an interactive software simulation and a Video Demo that can be published as an MP4 video. Then, you will approach the advanced responsive features of Captivate to create a project that can be viewed on any device. And finally, you will immerse your learners in a 360o environment by creating Virtual Reality projects of Adobe Captivate. At the end of the book, you will empower your workflow and projects with the newer and most advanced features of the application, including variables, advanced actions, JavaScript, and using Captivate 2019 with other applications. If you want to produce high quality eLearning content using a wide variety of techniques, implement eLearning in your company, enable eLearning on any device, assess the effectiveness of the learning by using extensive Quizzing features, or are simply interested in eLearning, this book has you covered! What you will learn * Learn how to use the objects in Captivate to build professional eLearning content * Enhance your projects by adding interactivity, animations, and more * Add multimedia elements, such as audio and video, to create engaging learning experiences * Use themes to craft a unique visual experience * Use question slides to create SCORM-compliant quizzes that integrate seamlessly with your LMS * Make your content fit any device with responsive features of Captivate * Create immersive 360° experiences with Virtual Reality projects of Captivate 2019 * Integrate Captivate with other applications (such as PowerPoint and Photoshop) to establish a professional eLearning production workflow * Publish your project in a wide variety of formats including HTML5 and Flash Who this book is for If you are a teacher, instructional designer, eLearning developer, or human resources manager who wants to implement eLearning, then this book is for you. A basic knowledge of your OS is all it takes to create the next generation of responsive eLearning content.

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries Key Features *Build a strong foundation in neural networks and deep learning with Python libraries *Explore advanced deep learning techniques and their applications across computer vision and NLP *Learn how a computer can navigate in complex environments with reinforcement learning Book Description With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications. What you will learn *Grasp the mathematical theory behind neural networks and deep learning processes *Investigate and resolve computer vision challenges using convolutional networks and capsule networks *Solve generative tasks using variational autoencoders and Generative Adversarial Networks *Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models *Explore reinforcement learning and understand how agents behave in a complex environment *Get up to date with applications of deep learning in autonomous vehicles Who this book is for This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.

A practical guide that helps you progress to using modern integration methods and leverage new cloud capability models Key Features * Design critical hybrid integration solutions for your organization * Gain in-depth knowledge of how to build cloud-native integration solutions * Leverage cognitive services to build smart cloud solutions Book Description With more enterprises adapting cloud-based and API-based solutions, application integration has become more relevant and significant than ever before. Parallelly, Serverless Integration has gained popularity, as it helps agile organizations to build integration solutions quickly without having to worry about infrastructure costs. With Microsoft Azure’s serverless offerings, such as Logic Apps, Azure Functions, API Management, Azure Event Grid and Service Bus, organizations can build powerful, secure, and scalable integration solutions with ease. The primary objective of this book is to help you to understand various serverless offerings included within Azure Integration Services, taking you through the basics and industry practices and patterns. This book starts by explaining the concepts of services such as Azure Functions, Logic Apps, and Service Bus with hands-on examples and use cases. After getting to grips with the basics, you will be introduced to API Management and building B2B solutions using Logic Apps Enterprise Integration Pack. This book will help readers to understand building hybrid integration solutions and touches upon Microsoft Cognitive Services and leveraging them in modern integration solutions. Industry practices and patterns are brought to light at appropriate opportunities while explaining various concepts. What you will learn * Learn about the design principles of Microsoft Azure Serverless Integration * Get insights into Azure Functions, Logic Apps, Azure Event Grid and Service Bus * Secure and manage your integration endpoints using Azure API Management * Build advanced B2B solutions using Logic Apps, Enterprise Integration Pack * Monitor integration solutions using tools available on the market * Discover design patterns for hybrid integration Who this book is for Serverless Integration Design Patterns with Azure is for you if you are a solution architect or integration professional aiming to build complex cloud solutions for your organization. Developers looking to build next-level hybrid or cloud solutions will also find this book useful. Prior programming knowledge is necessary.

Understand the nitty-gritty of Cybersecurity with ease Key Features * Align your security knowledge with industry leading concepts and tools * Acquire required skills and certifications to survive the ever changing market needs * Learn from industry experts to analyse, implement, and maintain a robust environment Book Description It's not a secret that there is a huge talent gap in the cybersecurity industry. Everyone is talking about it including the prestigious Forbes Magazine, Tech Republic, CSO Online, DarkReading, and SC Magazine, among many others. Additionally, Fortune CEO's like Satya Nadella, McAfee's CEO Chris Young, Cisco's CIO Colin Seward along with organizations like ISSA, research firms like Gartner too shine light on it from time to time. This book put together all the possible information with regards to cybersecurity, why you should choose it, the need for cyber security and how can you be part of it and fill the cybersecurity talent gap bit by bit. Starting with the essential understanding of security and its needs, we will move to security domain changes and how artificial intelligence and machine learning are helping to secure systems. Later, this book will walk you through all the skills and tools that everyone who wants to work as security personal need to be aware of. Then, this book will teach readers how to think like an attacker and explore some advanced security methodologies. Lastly, this book will deep dive into how to build practice labs, explore real-world use cases and get acquainted with various cybersecurity certifications. By the end of this book, readers will be well-versed with the security domain and will be capable of making the right choices in the cybersecurity field. What you will learn * Get an overview of what cybersecurity is and learn about the various faces of cybersecurity as well as identify domain that suits you best * Plan your transition into cybersecurity in an efficient and effective way * Learn how to build upon your existing skills and experience in order to prepare for your career in cybersecurity Who this book is for This book is targeted to any IT professional who is looking to venture in to the world cyber attacks and threats. Anyone with some understanding or IT infrastructure workflow will benefit from this book. Cybersecurity experts interested in enhancing their skill set will also find this book useful.

Plan and build useful machine learning systems for financial services, with full working Python code Key Features * Build machine learning systems that will be useful across the financial services industry * Discover how machine learning can solve finance industry challenges * Gain the machine learning insights and skills fintech companies value most Book Description Machine learning skills are essential for anybody working in financial data analysis. Machine Learning for Finance shows you how to build machine learning models for use in financial services organizations. It shows you how to work with all the key machine learning models, from simple regression to advanced neural networks. You will see how to use machine learning to automate manual tasks, identify and address systemic bias, and find new insights and patterns hidden in available data. Machine Learning for Finance encourages and equips you to find new ways to use data to serve an organization’s business goals. Broad in scope yet deeply practical in approach, Machine Learning for Finance will help you to apply machine learning in all parts of a financial organization’s infrastructure. If you work or plan to work in fintech, and want to gain one of the most valuable skills in the sector today, this book is for you. What you will learn * Practical machine learning for the finance sector * Build machine learning systems that support the goals of financial organizations * Think creatively about problems and how machine learning can solve them * Identify and reduce sources of bias from machine learning models * Apply machine learning to structured data, natural language, photographs, and written text related to finance * Use machine learning to detect fraud, forecast financial trends, analyze customer sentiments, and more * Implement heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlow Who this book is for Machine Learning for Finance is for financial professionals who want to develop and apply machine learning skills, and for students entering the field. You should be comfortable with Python and the basic data science stack, such as NumPy, pandas, and Matplotlib, to get the most out of this book.

This quick start guide will bring the readers to a basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and Clustering Key Features * Your handy guide to building machine learning workflows in Go for real-world scenarios * Build predictive models using the popular supervised and unsupervised machine learning techniques * Learn all about deployment strategies and take your ML application from prototype to production ready Book Description Machine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology. This book will teach you how to efficiently develop machine learning applications in Go. The book starts with an introduction to machine learning and its development process, explaining the types of problems that it aims to solve and the solutions it offers. It then covers setting up a frictionless Go development environment, including running Go interactively with Jupyter notebooks. Finally, common data processing techniques are introduced. The book then teaches the reader about supervised and unsupervised learning techniques through worked examples that include the implementation of evaluation metrics. These worked examples make use of the prominent open-source libraries GoML and Gonum. The book also teaches readers how to load a pre-trained model and use it to make predictions. It then moves on to the operational side of running machine learning applications: deployment, Continuous Integration, and helpful advice for effective logging and monitoring. At the end of the book, readers will learn how to set up a machine learning project for success, formulating realistic success criteria and accurately translating business requirements into technical ones. What you will learn * Understand the types of problem that machine learning solves, and the various approaches * Import, pre-process, and explore data with Go to make it ready for machine learning algorithms * Visualize data with gonum/plot and Gophernotes * Diagnose common machine learning problems, such as overfitting and underfitting * Implement supervised and unsupervised learning algorithms using Go libraries * Build a simple web service around a model and use it to make predictions Who this book is for This book is for developers and data scientists with at least beginner-level knowledge of Go, and a vague idea of what types of problem Machine Learning aims to tackle. No advanced knowledge of Go (and no theoretical understanding of the math that underpins Machine Learning) is required.

Build state-of-the-art web applications quickly and efficiently using Flask and related technologies with Python 3 Key Features * Updated to Flask 1.0.3 and Python 3.7 with coverage of Microservices * Get the most out of the powerful Flask framework and maintain the flexibility of your design choices * Write cleaner and maintainable code with the help of sample apps Book Description Flask, the lightweight Python web framework, is popular due to its powerful modular design that lets you build scalable web apps. With this recipe-based guide, you’ll explore modern solutions and best practices for Flask web development. Updated to the latest version of Flask and Python 3, this second edition of Flask Framework Cookbook moves away from some of the old and obsolete libraries and introduces recipes on bleeding edge technologies. You’ll discover different ways of using Flask to create, deploy, and manage microservices. This Flask Python book starts by covering the different configurations that a Flask application can make use of, and then helps you work with templates and learn about the ORM and view layers. You’ll also be able to write an admin interface and get to grips with debugging and logging errors. Finally, you’ll grasp a variety of deployment and post-deployment techniques for platforms such as Apache, Tornado, and Heroku. By the end of this book, you’ll have gained all the knowledge you need to write Flask applications in the best possible way and scale them using standard industry practices. What you will learn * Explore web application development in Flask, right from installation to post-deployment stages * Make use of advanced templating and data modeling techniques * Discover effective debugging, logging, and error handling techniques in Flask * Integrate Flask with different technologies such as Redis, Sentry, and MongoDB * Deploy and package Flask applications with Docker and Kubernetes * Design scalable microservice architecture using AWS LambdaContinuous integration and Continuous deployment Who this book is for If you are a web developer who wants to learn more about developing scalable and production-ready applications in Flask, this is the book for you. You’ll also find this book useful if you are already aware of Flask's major extensions and want to use them for better application development. Basic Python programming experience along with basic understanding of Flask is assumed.

Leverage the features of Azure SQL database and become an expert in data management Key Features * Explore ways to create shards and elastic pools to scale Azure SQL databases * Automate common management tasks with PowerShell * Implement over 40 practical activities and exercises to reinforce your learning Book Description Despite being the cloud version of SQL Server, Azure SQL Database differs in key ways when it comes to management, maintenance, and administration. This book shows you how to administer Azure SQL database to fully benefit from its wide range of features and functionality. Professional Azure SQL Database Administration begins by covering the architecture and explaining the difference between Azure SQL Database and the on-premise SQL Server to help you get comfortable with Azure SQL database. You’ll perform common tasks such as migrating, backing up, and restoring a SQL Server database to an Azure database. As you progress, you’ll study how you can save costs and manage and scale multiple SQL Databases using elastic pools. You’ll also implement a disaster recovery solution using standard and active geo-replication. Whether it is learning different techniques to monitor and tune an Azure SQL database or improving performance using in-memory technology, this book will enable you to make the most out of Azure SQL database features and functionality for data management solutions. By the end of this book, you’ll be well versed with key aspects of an Azure SQL database instance, such as migration, backup restorations, performance optimization, high availability, and disaster recovery. What you will learn * Understand Azure SQL Database configuration and pricing options * Provision a new SQL database or migrate an existing on-premise SQL Server database to Azure SQL Database * Back up and restore Azure SQL Database * Secure an Azure SQL database * Scale an Azure SQL database * Monitor and tune an Azure SQL database * Implement high availability and disaster recovery with Azure SQL Database * Automate common management tasks with PowerShell * Develop a scalable cloud solution with Azure SQL Database * Manage, maintain, and secure managed instances Who this book is for If you’re a database administrator, database developer, or an application developer interested in developing new applications or migrating existing ones with Azure SQL database, this book is for you. Prior experience of working with an on-premise SQL Server or Azure SQL database along with a basic understanding of PowerShell scripts and C# code is necessary to grasp the concepts covered in this book.

From learning about the most sought-after design patterns to a comprehensive coverage of architectural patterns and code testing, this book is all you need to write clean, reusable code Key Features *Write clean, reusable and maintainable code, and make the most of the latest Swift version. *Analyze case studies of some of the popular open source projects and give your workflow a huge boost *Choose patterns such as MVP, MVC, and MVVM depending on the application being built Book Description Swift keeps gaining traction not only amongst Apple developers but also as a server-side language. This book demonstrates how to apply design patterns and best practices in real-life situations, whether that's for new or already existing projects. You’ll begin with a quick refresher on Swift, the compiler, the standard library, and the foundation, followed by the Cocoa design patterns – the ones at the core of many cocoa libraries – to follow up with the creational, structural, and behavioral patterns as defined by the GoF. You'll get acquainted with application architecture, as well as the most popular architectural design patterns, such as MVC and MVVM, and learn to use them in the context of Swift. In addition, you’ll walk through dependency injection and functional reactive programming. Special emphasis will be given to techniques to handle concurrency, including callbacks, futures and promises, and reactive programming. These techniques will help you adopt a test-driven approach to your workflow in order to use Swift Package Manager and integrate the framework into the original code base, along with Unit and UI testing. By the end of the book, you'll be able to build applications that are scalable, faster, and easier to maintain. What you will learn *Work efficiently with Foundation and Swift Standard library *Understand the most critical GoF patterns and use them efficiently *Use Swift 4.2 and its unique capabilities (and limitations) to implement and improve GoF patterns *Improve your application architecture and optimize for maintainability and performance *Write efficient and clean concurrent programs using futures and promises, or reactive programming techniques *Use Swift Package Manager to refactor your program into reusable components *Leverage testing and other techniques for writing robust code Who this book is for This book is for intermediate developers who want to apply design patterns with Swift to structure and scale their applications. You are expected to have basic knowledge of iOS and Swift.

Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing. Key Features *Clear and concise explanations *Gives important insights into deep learning models *Practical demonstration of key concepts Book Description PyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without any hassle. We will set up the deep learning environment using PyTorch, and then train and deploy different types of deep learning models, such as CNN, RNN, and autoencoders. You will learn how to optimize models by tuning hyperparameters and how to use PyTorch in multiprocessor and distributed environments. We will discuss long short-term memory network (LSTMs) and build a language model to predict text. By the end of this book, you will be familiar with PyTorch's capabilities and be able to utilize the library to train your neural networks with relative ease. What you will learn *Set up the deep learning environment using the PyTorch library *Learn to build a deep learning model for image classification *Use a convolutional neural network for transfer learning *Understand to use PyTorch for natural language processing *Use a recurrent neural network to classify text *Understand how to optimize PyTorch in multiprocessor and distributed environments *Train, optimize, and deploy your neural networks for maximum accuracy and performance *Learn to deploy production-ready models Who this book is for Developers and Data Scientist familiar with Machine Learning but new to deep learning, or existing practitioners of deep learning who would like to use PyTorch to train their deep learning models will find this book to be useful. Having knowledge of Python programming will be an added advantage, while previous exposure to PyTorch is not needed.

Understand, explore, and effectively present data using the powerful data visualization techniques of Python Key Features *Use the power of Pandas and Matplotlib to easily solve data mining issues *Understand the basics of statistics to build powerful predictive data models *Grasp data mining concepts with helpful use-cases and examples Book Description Data mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining. You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models. By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional. This Learning Path includes content from the following Packt products: *Statistics for Machine Learning by Pratap Dangeti *Matplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin Yim *Pandas Cookbook by Theodore Petrou What you will learn *Understand the statistical fundamentals to build data models *Split data into independent groups *Apply aggregations and transformations to each group *Create impressive data visualizations *Prepare your data and design models *Clean up data to ease data analysis and visualization *Create insightful visualizations with Matplotlib and Seaborn *Customize the model to suit your own predictive goals Who this book is for If you want to learn how to use the many libraries of Python to extract impactful information from your data and present it as engaging visuals, then this is the ideal Learning Path for you. Some basic knowledge of Python is enough to get started with this Learning Path.

A comprehensive guide to building full stack applications covering frontend and server-side programming, data management, and web security Key Features * Unleash the power of React Hooks to build interactive and complex user interfaces * Build scalable full stack applications designed to meet demands of modern users * Understand how the Axios library simplifies CRUD operations Book Description React Hooks have changed the way React components are coded. They enable you to write components in a more intuitive way without using classes, which makes your code easier to read and maintain. Building on from the previous edition, this book is updated with React Hooks and the latest changes introduced in create-react-app and Spring Boot 2.1. This book starts with a brief introduction to Spring Boot. You’ll understand how to use dependency injection and work with the data access layer of Spring using Hibernate as the ORM tool. You’ll then learn how to build your own RESTful API endpoints for web applications. As you advance, the book introduces you to other Spring components, such as Spring Security to help you secure the backend. Moving on, you’ll explore React and its app development environment and components for building your frontend. Finally, you’ll create a Docker container for your application by implementing the best practices that underpin professional full stack web development. By the end of this book, you’ll be equipped with all the knowledge you need to build modern full stack applications with Spring Boot for the backend and React for the frontend. What you will learn * Create a RESTful web service with Spring Boot * Grasp the fundamentals of dependency injection and how to use it for backend development * Discover techniques for securing the backend using Spring Security * Understand how to use React for frontend programming * Benefit from the Heroku cloud server by deploying your application to it * Delve into the techniques for creating unit tests using JUnit * Explore the Material UI component library to make more user-friendly user interfaces Who this book is for If you are a Java developer familiar with Spring, but are new to building full stack applications, this is the book for you.

Apply cloud native patterns and practices to deliver responsive, resilient, elastic, and message-driven systems with confidence Key Features * Discover best practices for applying cloud native patterns to your cloud applications * Explore ways to effectively plan resources and technology stacks for high security and fault tolerance * Gain insight into core architectural principles using real-world examples Book Description Cloud computing has proven to be the most revolutionary IT development since virtualization. Cloud native architectures give you the benefit of more flexibility over legacy systems. This Learning Path teaches you everything you need to know for designing industry-grade cloud applications and efficiently migrating your business to the cloud. It begins by exploring the basic patterns that turn your database inside out to achieve massive scalability. You’ll learn how to develop cloud native architectures using microservices and serverless computing as your design principles. Then, you’ll explore ways to continuously deliver production code by implementing continuous observability in production. In the concluding chapters, you’ll learn about various public cloud architectures ranging from AWS and Azure to the Google Cloud Platform, and understand the future trends and expectations of cloud providers. By the end of this Learning Path, you’ll have learned the techniques to adopt cloud native architectures that meet your business requirements. This Learning Path includes content from the following Packt products: * Cloud Native Development Patterns and Best Practices by John Gilbert * Cloud Native Architectures by Erik Farr et al. What you will learn * Understand the difference between cloud native and traditional architecture * Automate security controls and configuration management * Minimize risk by evolving your monolithic systems into cloud native applications * Explore the aspects of migration, when and why to use it * Apply modern delivery and testing methods to continuously deliver production code * Enable massive scaling by turning your database inside out Who this book is for This Learning Path is designed for developers who want to progress into building cloud native systems and are keen to learn the patterns involved. Software architects, who are keen on designing scalable and highly available cloud native applications, will also find this Learning Path very useful. To easily grasp these concepts, you will need basic knowledge of programming and cloud computing.

Leverage the power of reward-based training for your deep learning models with Python Key Features * Understand Q-learning algorithms to train neural networks using Markov Decision Process (MDP) * Study practical deep reinforcement learning using Q-Networks * Explore state-based unsupervised learning for machine learning models Book Description Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers. This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you get familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into modelfree Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in real-world applications such as scientific research. Toward the end, you’ll gain a sense of what’s in store for reinforcement learning. By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow. What you will learn * Explore the fundamentals of reinforcement learning and the state-action-reward process * Understand Markov decision processes * Get well versed with libraries such as Keras, and TensorFlow * Create and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI Gym * Choose and optimize a Q-Network’s learning parameters and fine-tune its performance * Discover real-world applications and use cases of Q-learning Who this book is for If you are a machine learning developer, engineer, or professional who wants to delve into the deep learning approach for a complex environment, then this is the book for you. Proficiency in Python programming and basic understanding of decision-making in reinforcement learning is assumed.

Explore the exciting world of machine learning with the fastest growing technology in the world Key Features * Understand various machine learning concepts with real-world examples * Implement a supervised machine learning pipeline from data ingestion to validation * Gain insights into how you can use machine learning in everyday life Book Description Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own! What you will learn * Understand the concept of supervised learning and its applications * Implement common supervised learning algorithms using machine learning Python libraries * Validate models using the k-fold technique * Build your models with decision trees to get results effortlessly * Use ensemble modeling techniques to improve the performance of your model * Apply a variety of metrics to compare machine learning models Who this book is for Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.