Blockchain: A Guide to Understanding Blockchain

Publiseer: Free Book And Music Publishing

Alan Wake Guide

Asphalt 8: Airborne - Strategy Guide

Snapchat 101: An Easy Get Started Guide On Snapchatting

Learn How to MAKE ＆ USE Wordpress Blogs for Beginners

If your application source code is overly verbose, it can be a nightmare to maintain. Write concise and expressive, type-safe code in an environment that lets you build for the JVM, browser, and more. Key Features *Expert guidance that shows you to efficiently use both object-oriented and functional programming techniques *Understand functional programming libraries, such as Cats and Scalaz, and use them to augment your Scala development *Perfectly balances theory and hands-on exercises, assessments, and activities Book Description This book teaches you how to build and contribute to Scala programs, recognizing common patterns and techniques used with the language. You’ll learn how to write concise, functional code with Scala. After an introduction to core concepts, syntax, and writing example applications with scalac, you’ll learn about the Scala Collections API and how the language handles type safety via static types out-of-the-box. You’ll then learn about advanced functional programming patterns, and how you can write your own Domain Specific Languages (DSLs). By the end of the book, you’ll be equipped with the skills you need to successfully build smart, efficient applications in Scala that can be compiled to the JVM. What you will learn *Understand the key language syntax and core concepts for application development *Master the type system to create scalable type-safe applications while cutting down your time spent debugging *Understand how you can work with advanced data structures via built-in features such as the Collections library *Use classes, objects, and traits to transform a trivial chatbot program into a useful assistant *Understand what are pure functions, immutability, and higher-order functions *Recognize and implement popular functional programming design patterns Who this book is for This is an ideal book for developers who are looking to learn Scala, and is particularly well suited for Java developers looking to migrate across to Scala for application development on the JVM.

Build smart applications by implementing real-world artificial intelligence projects Key Features *Explore a variety of AI projects with Python *Get well-versed with different types of neural networks and popular deep learning algorithms *Leverage popular Python deep learning libraries for your AI projects Book Description Artificial Intelligence (AI) is the newest technology that’s being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence. This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library. By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progress What you will learn *Build a prediction model using decision trees and random forest *Use neural networks, decision trees, and random forests for classification *Detect YouTube comment spam with a bag-of-words and random forests *Identify handwritten mathematical symbols with convolutional neural networks *Revise the bird species identifier to use images *Learn to detect positive and negative sentiment in user reviews Who this book is for Python Artificial Intelligence Projects for Beginners is for Python developers who want to take their first step into the world of Artificial Intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you’re able to play around with code

Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction. About This Book ? Get up and running with the Jupyter ecosystem and some example datasets ? Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests ? Discover how you can use web scraping to gather and parse your own bespoke datasets Who This Book Is For This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start. What You Will Learn ? Identify potential areas of investigation and perform exploratory data analysis ? Plan a machine learning classification strategy and train classification models ? Use validation curves and dimensionality reduction to tune and enhance your models ? Scrape tabular data from web pages and transform it into Pandas DataFrames ? Create interactive, web-friendly visualizations to clearly communicate your findings In Detail Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context. Style and approach This book covers every aspect of the standard data-workflow process within a day, along with theory, practical hands-on coding, and relatable illustrations.

Automate the deployment of your environment with the power of the Docker toolchain About This Book ? Written and reviewed by expert Docker developers ? The book precisely covers all the essential topics such as building images and managing container swarms required in day to day use for Docker ? The book includes activities on the docker CLI and exercises such as writing Dockerfiles for Python which will allow you to reinforce the concepts covered Who This Book Is For The book is crafted for developers, system architects, junior and mid-level site reliability engineers interested in adapting a docker workflow. They are also required to have a basic knowledge of UNIX concepts such as ssh, ports and logs What You Will Learn ? Understand how to effectively design and build containers for different applications ? Setup an environment for testing, avoiding environment mismatch that is breaking production ? Setup and manage a multi-tier environment ? Run, debug, and experiment with applications in a container In Detail DevOps with Docker outlines the power of containerization and the influence this innovation has on development teams and general operations. We also get to understand what DevOps really is, the principles involved and how the process contributes to product health, by implementing a Docker workflow. We will learn to interpret Dockerfile syntax, build images and setup containers and images. In addition, we will deploy a Docker image to the Docker Hub.Docker is an open source containerization tool, that makes it easier to streamline product delivery. It helps reduce the time taken to get from a whiteboard sketch of the business to a money-back implementation. This fast-paced book is a perfect amalgamation of theory and hands-on exercises. The book will take you through the basics of Docker and DevOps and why and how they integrate. You will then understand what containers are, and how to create and manage them. Next, we will work on the docker-compose file and CLI. Then we will move to set up a network with the docker-compose tool. Gradually you will learn how to scale a delivery pipeline and multiple deployments with Docker. Lastly, you will grasp the concept of orchestration and learn to implement the delivery of containerized applications. Style and approach This is a fast-paced, practical hands-on book aimed at experienced developers and system architects. As you progress you’ll find helpful tips and tricks, as well as useful self-assessment material, exercises and activities to help benchmark your progress and reinforce what you’ve learned. The activities are devised to simulate the real-world conditions in order to equip you with the necessary skills to accelerate software deployment while still retaining security, portability and saving on costs.

Master the fundamentals of programming in Swift 4 About This Book ? Covers theory and practice in equal parts ? Teaches you how to correctly structure and architect software using Swift ? Uses real-world examples to connect the theory to a professional setting ? Imparts expertise in the core Swift standard library Who This Book Is For If you are seeking fundamental Swift programming skills, in preparation for learning to develop native applications for iOS or macOS, this book is the best for you. You don’t need to have any prior Swift knowledge; however, object-oriented programming experience is desired. What You Will Learn ? Explore the fundamental Swift programming concepts, language structure, and the Swift programming syntax ? Learn how Swift compares to other computer languages and how to transform your thinking to leverage new concepts such as optionals and protocols ? Master how to use key language elements, such as strings and collections ? Grasp how Swift supports modern application development using advanced features, such as built-in Unicode support and higher-order functions. In Detail Take your first foray into programming for Apple devices with Swift. Swift is fundamentally different from Objective-C, as it is a protocol-oriented language. While you can still write normal object-oriented code in Swift, it requires a new way of thinking to take advantage of its powerful features and a solid understanding of the basics to become productive. This course helps you develop client-side and server-side applications, as well as web services using Swift. We'll begin with exploring the fundamental Swift programming concepts, language structure, and the Swift programming syntax. Then, we'll learn to create original custom operators with Swift operators, branching, and loops. Moving on, we'll learn how to run application codes and compile errors. Having made progress with it, we'll see how Swift compares to other computer languages and how to transform your thinking. Then, master the usage of key language elements, such as strings and collections. Finally, grasp how Swift supports modern application development using advanced features, such as built-in Unicode support and higher-order functions. This is an introductory course to the Swift programming language with Xcode.After completing this course, students will be well-prepared to begin developing native end-user applications for iOS or macOS, or to develop server-side (back-end) application and web services using Swift on Linux. Style and approach This is an introductory course to the Swift programming language with Xcode. The course does not expect you to have any previous Swift knowledge or experience. The course covers ample amount of exercises so that students learn the basics hands-on.

Foster your NLP applications with the help of deep learning, NLTK, and TensorFlow Key Features * Weave neural networks into linguistic applications across various platforms * Perform NLP tasks and train its models using NLTK and TensorFlow * Boost your NLP models with strong deep learning architectures such as CNNs and RNNs Book Description Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges. To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts. What you will learn *Implement semantic embedding of words to classify and find entities *Convert words to vectors by training in order to perform arithmetic operations *Train a deep learning model to detect classification of tweets and news *Implement a question-answer model with search and RNN models *Train models for various text classification datasets using CNN *Implement WaveNet a deep generative model for producing a natural-sounding voice *Convert voice-to-text and text-to-voice *Train a model to convert speech-to-text using DeepSpeech Who this book is for Hands-on Natural Language Processing with Python is for you if you are a developer, machine learning or an NLP engineer who wants to build a deep learning application that leverages NLP techniques. This comprehensive guide is also useful for deep learning users who want to extend their deep learning skills in building NLP applications. All you need is the basics of machine learning and Python to enjoy the book.

Learn everything you need to get up and running with cutting-edge API development using JavaScript and Node.js; ideal for data-intensive real-time applications that run across multiple platforms. Key Features *Build web APIs from start to finish using JavaScript across the development stack *Explore advanced concepts such as authentication with JWT, and running tests against your APIs *Implement over 20 practical activities and exercises across 9 topics to reinforce your learning Book Description Using the same framework to build both server and client-side applications saves you time and money. This book teaches you how you can use JavaScript and Node.js to build highly scalable APIs that work well with lightweight cross-platform client applications. It begins with the basics of Node.js in the context of backend development, and quickly leads you through the creation of an example client that pairs up with a fully authenticated API implementation. By the end of the book, you’ll have the skills and exposure required to get hands-on with your own API development project. What you will learn *Understand how Node.js works, its trends, and where it is being used now *Learn about application modularization and built-in Node.js modules *Use the npm third-party module registry to extend your application *Gain an understanding of asynchronous programming with Node.js *Develop scalable and high-performing APIs using hapi.js and Knex.js *Write unit tests for your APIs to ensure reliability and maintainability Who this book is for This book is ideal for developers who already understand JavaScript and are looking for a quick no-frills introduction to API development with Node.js. Though prior experience with other server-side technologies such as Python, PHP, ASP.NET, Ruby will help, it’s not essential to have a background in backend development before getting started.

Install and configure a pfSense router/firewall, and become a pfSense expert in the process. About This Book ? You can always do more to secure your software – so extend and customize your pfSense firewall ? Build a high availability security system that’s fault-tolerant – and capable of blocking potential threats ? Put the principles of better security into practice by implementing examples provided in the text Who This Book Is For This book is for those with at least an intermediate understanding of networking. Prior knowledge of pfSense would be helpful but is not required. Those who have the resources to set up a pfSense firewall, either in a real or virtual environment, will especially benefit, as they will be able to follow along with the examples in the book. What You Will Learn ? Configure pfSense services such as DHCP, Dynamic DNS, captive portal, DNS, NTP and SNMP ? Set up a managed switch to work with VLANs ? Use pfSense to allow, block and deny traffic, and to implement Network Address Translation (NAT) ? Make use of the traffic shaper to lower and raise the priority of certain types of traffic ? Set up and connect to a VPN tunnel with pfSense ? Incorporate redundancy and high availability by utilizing load balancing and the Common Address Redundancy Protocol (CARP) ? Explore diagnostic tools in pfSense to solve network problems In Detail pfSense has the same reliability and stability as even the most popular commercial firewall offerings on the market – but, like the very best open-source software, it doesn’t limit you. You’re in control – you can exploit and customize pfSense around your security needs. Mastering pfSense - Second Edition, covers features that have long been part of pfSense such as captive portal, VLANs, traffic shaping, VPNs, load balancing, Common Address Redundancy Protocol (CARP), multi-WAN, and routing. It also covers features that have been added with the release of 2.4, such as support for ZFS partitions and OpenVPN 2.4. This book takes into account the fact that, in order to support increased cryptographic loads, pfSense version 2.5 will require a CPU that supports AES-NI. The second edition of this book places more of an emphasis on the practical side of utilizing pfSense than the previous edition, and, as a result, more examples are provided which show in step-by-step fashion how to implement many features. Style and approach Practical guide to learn the advanced functionalities of pfSense with minimum fuss.

Bridge the gap between developer and data scientist by creating a modern open-source, Python-based toolset that works with Jupyter Notebook, and PixieDust. Key Features *Think deeply as a developer about your strategy and toolset in data science *Discover the best tools that will suit you as a developer in your data analysis *Accelerate the road to data insight as a programmer using Jupyter Notebook *Deep dive into multiple industry data science use cases Book Description Thoughtful Data Science brings new strategies and a carefully crafted programmer's toolset to work with modern, cutting-edge data analysis. This new approach is designed specifically to give developers more efficiency and power to create cutting-edge data analysis and artificial intelligence insights. Industry expert David Taieb bridges the gap between developers and data scientists by creating a modern open-source, Python-based toolset that works with Jupyter Notebook, and PixieDust. You'll find the right balance of strategic thinking and practical projects throughout this book, with extensive code files and Jupyter projects that you can integrate with your own data analysis. David Taieb introduces four projects designed to connect developers to important industry use cases in data science. The first is an image recognition application with TensorFlow, to meet the growing importance of AI in data analysis. The second analyses social media trends to explore big data issues and natural language processing. The third is a financial portfolio analysis application using time series analysis, pivotal in many data science applications today. The fourth involves applying graph algorithms to solve data problems. Taieb wraps up with a deep look into the future of data science for developers and his views on AI for data science. What you will learn *Bridge the gap between developer and data scientist with a Python-based toolset *Get the most out of Jupyter Notebooks with new productivity-enhancing tools *Explore and visualize data using Jupyter Notebooks and PixieDust *Work with and assess the impact of artificial intelligence in data science *Work with TensorFlow, graphs, natural language processing, and time series *Deep dive into multiple industry data science use cases *Look into the future of data analysis and where to develop your skills Who this book is for This book is for established developers who want to bridge the gap between programmers and data scientists. With the introduction of PixieDust from its creator, the book will also be a great desk companion for the already accomplished Data Scientist. Some fluency in data interpretation and visualization is also assumed since this book addresses data professionals such as business and general data analysts. It will be helpful to have some knowledge of Python, using Python libraries, and some proficiency in web development.

This book gets you started and gives you knowledge about AWS Fargate in order to successfully incorporate it in your ECS container application. Key Features *Gives you a quick walk-through over the Amazon Elastic Container Services (ECS) *Provides an in depth knowledge of the components that Amazon Fargate has to offer. *Learn the practical aspects of Docker application development with a managed service Book Description Amazon Fargate is new launch type for the Amazon Elastic Container Service (ECS). ECS is an AWS service for Docker container orchestration. Docker is the de facto containerization framework and has revolutionized packaging and deployment of software. The introduction of Fargate has made the ECS platform serverless. The book takes you through how Amazon Fargate runs ECS services composed of tasks and Docker containers and exposes the containers to the user. Fargate has simplified the ECS platform. We will learn how Fargate creates an Elastic Network Interface (ENI) for each task and how auto scaling can be enabled for ECS tasks. You will also learn about using an IAM policy to download Docker images and send logs to CloudWatch. Finally, by the end of this book, you will have learned about how to use ECS CLI to create an ECS cluster and deploy tasks with Docker Compose. What you will learn *Running Docker containers with a managed service *Use Amazon ECS in Fargate launch mode *Configure CloudWatch Logging with Fargate *Use an IAM Role with Fargate *Understand how ECS CLI is used with Fargate *Learn how to use an Application Load Balancer with Fargate *Learn about Auto Scaling with Fargate Who this book is for This book is for Docker users and developers who want to learn about the Fargate platform. Typical job roles for which the book is suitable are DevOps Architect, Docker Engineer, and AWS Cloud Engineer. Prior knowledge of AWS and ECS is helpful but not mandatory.

Find out how to write Kotlin code without overhead and how to use different profiling tools and bytecode viewer to inspect expressions of Kotlin language. About This Book ? Apply modern Kotlin features to speed up processing and implement highly efficient and reliable codes. ? Learn memory optimization, concurrency, multi-threading, scaling, and caching techniques to achieve high performance. ? Learn how to prevent unnecessary overhead and use profiling tools to detect performance issues. Who This Book Is For This book is for Kotlin developers who would like to build reliable and high-performance applications. Prior Kotlin programming knowledge is assumed. What You Will Learn ? Understand the importance of high performance ? Learn performance metrics ? Learn popular design patterns currently being used in Kotlin ? Understand how to apply modern Kotlin features to data processing ? Learn how to use profling tools ? Discover how to read bytecode ? Learn to perform memory optimizations ? Uncover approaches to the multithreading environment In Detail The ease with which we write applications has been increasing, but with it comes the need to address their performance. A balancing act between easily implementing complex applications and keeping their performance optimal is a present-day requirement In this book, we explore how to achieve this crucial balance, while developing and deploying applications with Kotlin. The book starts by analyzing various Kotlin specifcations to identify those that have a potentially adverse effect on performance. Then, we move on to monitor techniques that enable us to identify performance bottlenecks and optimize performance metrics. Next, we look at techniques that help to us achieve high performance: memory optimization, concurrency, multi threading, scaling, and caching. We also look at fault tolerance solutions and the importance of logging. We'll also cover best practices of Kotlin programming that will help you to improve the quality of your code base. By the end of the book, you will have gained some insight into various techniques and solutions that will help to create high-performance applications in the Kotlin environment Style and approach This book guides you through how to use profiling tools to detect performance issues and build high-performance applications in the Kotlin environment.

Explore TensorFlow's capabilities to perform efficient deep learning on images Key Features *Discover image processing for machine vision *Build an effective image classification system using the power of CNNs *Leverage TensorFlow’s capabilities to perform efficient deep learning Book Description TensorFlow is Google’s popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks. Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras. What you will learn *Build machine learning models particularly focused on the MNIST digits *Work with Docker and Keras to build an image classifier *Understand natural language models to process text and images *Prepare your dataset for machine learning *Create classical, convolutional, and deep neural networks *Create a RESTful image classification server Who this book is for Hands-On Deep Learning for Images with TensorFlow is for you if you are an application developer, data scientist, or machine learning practitioner looking to integrate machine learning into application software and master deep learning by implementing practical projects in TensorFlow. Knowledge of Python programming and basics of deep learning are required to get the best out of this book.

Learn how to create interactive and visually aesthetic plots using the Bokeh package in Python About This Book ? A step by step approach to creating interactive plots with Bokeh ? Go from nstallation all the way to deploying your very own Bokeh application ? Work with a real time datasets to practice and create your very own plots and applications Who This Book Is For This book is well suited for data scientists and data analysts who want to perform interactive data visualization on their web browsers using Bokeh. Some exposure to Python programming will be helpful, but prior experience with Bokeh is not required. What You Will Learn ? Installing Bokeh and understanding its key concepts ? Creating plots using glyphs, the fundamental building blocks of Bokeh ? Creating plots using different data structures like NumPy and Pandas ? Using layouts and widgets to visually enhance your plots and add a layer of interactivity ? Building and hosting applications on the Bokeh server ? Creating advanced plots using spatial data In Detail Adding a layer of interactivity to your plots and converting these plots into applications hold immense value in the field of data science. The standard approach to adding interactivity would be to use paid software such as Tableau, but the Bokeh package in Python offers users a way to create both interactive and visually aesthetic plots for free. This book gets you up to speed with Bokeh - a popular Python library for interactive data visualization. The book starts out by helping you understand how Bokeh works internally and how you can set up and install the package in your local machine. You then use a real world data set which uses stock data from Kaggle to create interactive and visually stunning plots. You will also learn how to leverage Bokeh using some advanced concepts such as plotting with spatial and geo data. Finally you will use all the concepts that you have learned in the previous chapters to create your very own Bokeh application from scratch. By the end of the book you will be able to create your very own Bokeh application. You will have gone through a step by step process that starts with understanding what Bokeh actually is and ends with building your very own Bokeh application filled with interactive and visually aesthetic plots. Style and approach This books take you through a step by step process that starts with understanding what Bokeh actually is and ends with building your very own Bokeh application filled with interactive and visually aesthetic plots that will amaze your users.

Test your C++ programming skills by solving real-world programming problems covered in the book About This Book ? Solve a variety of real-world programming and logic problems by leveraging the power of C++17 ? Test your skills in using language features, algorithms, data structures, design patterns, and more ? Explore areas such as cryptography, communication, and image handling in C++ Who This Book Is For This book will appeal to C++ developers of all levels. There's a challenge inside for everyone. What You Will Learn ? Serialize and deserialize JSON and XML data ? Perform encryption and signing to facilitate secure communication between parties ? Embed and use SQLite databases in your applications ? Use threads and asynchronous functions to implement generic purpose parallel algorithms ? Compress and decompress files to/from a ZIP archive ? Implement data structures such as circular buffer and priority queue ? Implement general purpose algorithms as well as algorithms that solve specific problems ? Create client-server applications that communicate over TCP/IP ? Consume HTTP REST services ? Use design patterns to solve real-world problems In Detail C++ is one of the most widely-used programming languages and has applications in a variety of fields, such as gaming, GUI programming, and operating systems, to name a few. Through the years, C++ has evolved into (and remains) one of the top choices for software developers worldwide. This book will show you some notable C++ features and how to implement them to meet your application needs. Each problem is unique and doesn't just test your knowledge of the language; it tests your ability to think out of the box and come up with the best solutions. With varying levels of difficulty, you'll be faced with a wide variety of challenges. And in case you're stumped, you don't have to worry: we've got the best solutions to the problems in the book. So are you up for the challenge? Style and approach A recipe-based approach where each problem is solved with the help of step by step instructions.

Transform games into environments using machine learning and Deep learning with Tensorflow, Keras, and Unity About This Book ? Learn how to apply core machine learning concepts to your games with Unity ? Learn the Fundamentals of Reinforcement Learning and Q-Learning and apply them to your games ? Learn How to build multiple asynchronous agents and run them in a training scenario Who This Book Is For This book is intended for developers with an interest in using Machine learning algorithms to develop better games and simulations with Unity. The reader will be required to have a working knowledge of C# and a basic understanding of Python. What You Will Learn ? Develop Reinforcement and Deep Reinforcement Learning for games. ? Understand complex and advanced concepts of reinforcement learning and neural networks ? Explore various training strategies for cooperative and competitive agent development ? Adapt the basic script components of Academy, Agent, and Brain to be used with Q Learning. ? Enhance the Q Learning model with improved training strategies such as Greedy-Epsilon exploration ? Implement a simple NN with Keras and use it as an external brain in Unity ? Understand how to add LTSM blocks to an existing DQN ? Build multiple asynchronous agents and run them in a training scenario In Detail Unity Machine Learning agents allow researchers and developers to create games and simulations using the Unity Editor, which serves as an environment where intelligent agents can be trained with machine learning methods through a simple-to-use Python API. This book takes you from the basics of Reinforcement and Q Learning to building Deep Recurrent Q-Network agents that cooperate or compete in a multi-agent ecosystem. You will start with the basics of Reinforcement Learning and how to apply it to problems. Then you will learn how to build self-learning advanced neural networks with Python and Keras/TensorFlow. From there you move o n to more advanced training scenarios where you will learn further innovative ways to train your network with A3C, imitation, and curriculum learning models. By the end of the book, you will have learned how to build more complex environments by building a cooperative and competitive multi-agent ecosystem. Style and approach This book focuses on the foundations of ML, RL and DL for building agents in a game or simulation