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Practical Rust Projects
Practical Rust Projects

Go beyond the basics and build complete applications using the Rust programming language. The applications in this book include a high-performance web client, a microcontroller (for a robot, for example), a game, an app that runs on Android, and an application that incorporates AI and machine learning. Each chapter will be organized in the following format: what this kind of application looks like; requirements and user stories of our example program; an introduction to the Rust libraries used; the actual implementation of the example program, including common pitfalls and their solutions; and a brief comparison of libraries for building each application, if there is no clear winner. Practical Rust Projects will open your eyes to the world of practical applications of Rust. After reading the book, you will be able to apply your Rust knowledge to build your own projects. Write Rust code that runs on microcontrollers; Build a 2D game; Create Rust-based mobile Android applicati ...
Next-Generation Machine Learning with Spark
Next-Generation Machine Learning with Spark

Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications. The past decade has seen an astonishing series of advances in machine learning. These breakthroughs are disrupting our everyday life and making an impact across every industry. Next-Generation Machine Learning with Spark provides a gentle introduction to Spark and Spark MLlib and advances to more powerful, third-party machine learning algorithms and libraries beyond what is available in the standard Spark MLlib library. By the end of this book, you will be able to apply your knowledge to real-world use cases through dozens of practical examples and insightful explanations. Be introduced to machine learning, Spark, and Spark MLlib 2.4.x; Achieve lightning-fast gradient boosting on Spark with the XGBoost4J-Spark and LightGBM libraries; Detect anomalies with the Isolation Forest algorithm for Spark; Use the Spark NLP and Stanford CoreNLP lib ...
Building Machine Learning Powered Applications
Building Machine Learning Powered Applications

Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers - including experienced practitioners and novices alike - will learn the tools, best practices, and challenges involved in building a real-world ML application step by step. Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies. This book will help you: Define your product goal and set up ...
Introducing Machine Learning
Introducing Machine Learning

Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project. Next, they introduce Microsoft's powerful ML.NET library, including capabilities for data processing, training, and evaluation. They present families of algorithms that can be trained to solve real-life problems, as well as deep learning techniques utilizing neural networks. The authors conclude by introducing valuable runtime services available through the Azure cloud platform and consider the long-term business vision for machine learning. 14-time Microsoft MVP Dino Esposito and Francesco Esposito help you; Explore what's known about how humans learn and how intelligent software is built; Discover which problems machine learning can address; Understand the machine learning pipeline: the steps lea ...
Mastering Machine Learning Algorithms, 2nd Edition
Mastering Machine Learning Algorithms, 2nd Edition

Mastering Machine Learning Algorithms, 2nd Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem - including NumPy and Keras - to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensem ...
Artificial Intelligence By Example, 2nd Edition
Artificial Intelligence By Example, 2nd Edition

Artificial intelligence (AI) has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervise ...
Learn TensorFlow 2.0
Learn TensorFlow 2.0

Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. Review the new features of TensorFlow 2.0; Use TensorFlow 2.0 to build machine learning and deep learning models; Perform sequence prediction ...
Apple Device Management
Apple Device Management

Working effectively with Apple platforms at a corporate or business level includes not only infrastructure, but a mode of thinking that administrators have to adopt to find success. A mode of thinking that forces you to leave 30 years of IT dogma at the door. This book is a guide through how to integrate Apple products in your environment with a minimum of friction. Because the Apple ecosystem is not going away. You'll start by understanding where Apple, third-party software vendors, and the IT community is taking us. What is Mobile Device Management and how does it work under the hood. By understanding how MDM works, you will understand what needs to happen on your networks in order to allow for MDM, as well as the best way to give the least amount of access to the servers or services that's necessary. You'll then look at management agents that do not include MDM, as well as when you will need to use an agent as opposed to when to use other options. Once you can install a managemen ...
Automated Machine Learning
Automated Machine Learning

This book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for pract ...
Python Machine Learning Projects
Python Machine Learning Projects

As machine learning is increasingly leveraged to find patterns, conduct analysis, and make decisions - sometimes without final input from humans who may be impacted by these findings - it is crucial to invest in bringing more stakeholders into the fold. This book of Python projects in machine learning tries to do just that: to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning to help ensure that it is serving us all. This book will set you up with a Python programming environment if you don't have one already, then provide you with a conceptual understanding of machine learning in the chapter "An Introduction to Machine Learning." What follows next are three Python machine learning projects. They will help you create a machine learning classifier, build a neural network to recognize handwritten digits, and give you a background in deep reinforcement learning through building a bot for Atari. ...
AI Crash Course
AI Crash Course

Welcome to the Robot World ... and start building intelligent software now! Through his best-selling video courses, Hadelin de Ponteves has taught hundreds of thousands of people to write AI software. Now, for the first time, his hands-on, energetic approach is available as a book. Starting with the basics before easing you into more complicated formulas and notation, AI Crash Course gives you everything you need to build AI systems with reinforcement learning and deep learning. Five full working projects put the ideas into action, showing step-by-step how to build intelligent software using the best and easiest tools for AI programming, including Python, TensorFlow, Keras, and PyTorch. AI Crash Course teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination. ...
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