Mastering TensorFlow 1.x. Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras

Armando Fandango

TensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs.This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Throughout the book, you will obtain hands-on experience with varied datasets, such as MNIST, CIFAR-10, PTB, text8, and COCO-Images.You will learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF Clusters, deploy production models with TensorFlow Serving, and build and deploy TensorFlow models for mobile and embedded devices on Android and iOS platforms. You will see how to call TensorFlow and Keras API within the R statistical software, and learn the required techniques for debugging when the TensorFlow API-based code does not work as expected.The book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. By the end of this guide, you will have mastered the offerings of TensorFlow and Keras, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems.

Rust Quick Start Guide. The easiest way to learn Rust programming

Daniel Arbuckle

Rust is an emerging programming language applicable to areas such as embedded programming, network programming, system programming, and web development. This book will take you from the basics of Rust to a point where your code compiles and does what you intend it to do!This book starts with an introduction to Rust and how to get set for programming, including the rustup and cargo tools for managing a Rust installation and development work?ow.Then you'll learn about the fundamentals of structuring a Rust program, such as functions, mutability, data structures, implementing behavior for types, and many more. You will also learn about concepts that Rust handles differently from most other languages.After understanding the Basics of Rust programming, you will learn about the core ideas, such as variable ownership, scope, lifetime, and borrowing. After these key ideas, you will explore making decisions in Rust based on data types by learning about match and if let expressions. After that, you'll work with different data types in Rust, and learn about memory management and smart pointers.

Kubernetes on AWS. Deploy and manage production-ready Kubernetes clusters on AWS

Ed Robinson

Docker containers promise to radicalize the way developers and operations build, deploy, and manage applications running on the cloud. Kubernetes provides the orchestration tools you need to realize that promise in production.Kubernetes on AWS guides you in deploying a production-ready Kubernetes cluster on the AWS platform. You will then discover how to utilize the power of Kubernetes, which is one of the fastest growing platforms for production-based container orchestration, to manage and update your applications. Kubernetes is becoming the go-to choice for production-grade deployments of cloud-native applications. This book covers Kubernetes from first principles. You will start by learning about Kubernetes' powerful abstractions - Pods and Services - that make managing container deployments easy. This will be followed by a guided tour through setting up a production-ready Kubernetes cluster on AWS, while learning the techniques you need to successfully deploy and manage your own applications.By the end of the book, you will have gained plenty of hands-on experience with Kubernetes on Amazon Web Services. You will also have picked up some tips on deploying and managing applications, keeping your cluster and applications secure, and ensuring that your whole system is reliable and resilient to failure.

Blockchain Development with Hyperledger. Build decentralized applications with Hyperledger Fabric and Composer

Salman A. Baset, Luc Desrosiers, Nitin Gaur, Petr Novotny, ...

Blockchain and Hyperledger are open source technologies that power the development of decentralized applications. This Learning Path is your helpful reference for exploring and building blockchain networks using Ethereum, Hyperledger Fabric, and Hyperledger Composer.Blockchain Development with Hyperledger will start off by giving you an overview of blockchain and demonstrating how you can set up an Ethereum development environment for developing, packaging, building, and testing campaign-decentralized applications. You'll then explore the de facto language Solidity, which you can use to develop decentralized applications in Ethereum. Following this, you'll be able to configure Hyperledger Fabric and use it to build private blockchain networks and applications that connect to them. Toward the later chapters, you'll learn how to design and launch a network, and even implement smart contracts in chain code. By the end of this Learning Path, you'll be able to build and deploy your own decentralized applications by addressing the key pain points encountered in the blockchain life cycle.This Learning Path includes content from the following Packt products:• Blockchain Quick Start Guide by Xun (Brian) Wu and Weimin Sun• Hands-On Blockchain with Hyperledger by Nitin Gaur et al.

Neural Networks with R. Build smart systems by implementing popular deep learning models in R

Balaji Venkateswaran, Giuseppe Ciaburro

Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning.This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you’ll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases.By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.

C# 7.1 and .NET Core 2.0 - Modern Cross-Platform Development. Create powerful applications with .NET Standard 2.0, ASP.NET Core 2.0, and Entity Framework Core 2.0, using Visual Studio 2017 or Visual Studio Code - Third Edition

Mark J. Price

C# 7.1 and .NET Core 2.0 – Modern Cross-Platform Development, Third Edition, is a practical guide to creating powerful cross-platform applications with C# 7.1 and .NET Core 2.0. It gives readers of any experience level a solid foundation in C# and .NET. The first part of the book runs you through the basics of C#, as well as debugging functions and object-oriented programming, before taking a quick tour through the latest features of C# 7.1 such as default literals, tuples, inferred tuple names, pattern matching, out variables, and more.After quickly taking you through C# and how .NET works, this book dives into the .NET Standard 2.0 class libraries, covering topics such as packaging and deploying your own libraries, and using common libraries for working with collections, performance, monitoring, serialization, files, databases, and encryption. The final section of the book demonstrates the major types of application that you can build and deploy cross-device and cross-platform. In this section, you'll learn about websites, web applications, web services, Universal Windows Platform (UWP) apps, and mobile apps. By the end of the book, you'll be armed with all the knowledge you need to build modern, cross-platform applications using C# and .NET.

C++ Data Structures and Algorithms. Learn how to write efficient code to build scalable and robust applications in C++

Wisnu Anggoro

C++ is a general-purpose programming language which has evolved over the years and is used to develop software for many different sectors. This book will be your companion as it takes you through implementing classic data structures and algorithms to help you get up and running as a confident C++ programmer.We begin with an introduction to C++ data structures and algorithms while also covering essential language constructs. Next, we will see how to store data using linked lists, arrays, stacks, and queues. Then, we will learn how to implement different sorting algorithms, such as quick sort and heap sort. Along with these, we will dive into searching algorithms such as linear search, binary search and more. Our next mission will be to attain high performance by implementing algorithms to string datatypes and implementing hash structures in algorithm design. We'll also analyze Brute Force algorithms, Greedy algorithms, and more.By the end of the book, you'll know how to build components that are easy to understand, debug, and use in different applications.

Python Machine Learning Blueprints. Put your machine learning concepts to the test by developing real-world smart projects - Second Edition

Alexander Combs, Michael Roman

Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects.The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you’ll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you’ll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you’ll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you’ll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks.By the end of this book, you’ll be able to analyze data seamlessly and make a powerful impact through your projects.