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
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 ...
The Cult of Mac, 2nd Edition
Apple is a global luxury brand whose products range from mobile phones and tablets to streaming TVs and smart home speakers. Yet despite this dominance, a distinct subculture persists, which celebrates the ways in which Apple products seem to encourage self-expression, identity, and innovation.
The beautifully designed second edition of The Cult of Mac takes you inside today's Apple fandom to explore how devotions - new and old - keep the fire burning. Join journalists Leander Kahney and David Pierini as they explore how enthusiastic fans line up for the latest product releases, and how artists pay tribute to Steve Jobs' legacy in sculpture and opera. Learn why some photographers and filmmakers have eschewed traditional gear in favor of iPhone cameras. Discover a community of collectors around the world who spend tens of thousands of dollars to buy, restore, and enshrine Apple artifacts, like the Newton MessagePad and Apple II.
Whether you're an Apple fan or just a casual ...
Machine Learning with R, 3rd Edition
Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.
Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.
This new 3rd edition updates the classic R data science book with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R. ...
Machine Learning for Finance
Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself.
The book is based on Jannes Klaas experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways.
The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and prob ...
Genetic Algorithms and Machine Learning for Programmers
Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you.
Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems.
Use heuristics and design fitness functions; Build genetic algorithms; Make nature-inspired swarms with ants, bees and particles; Create Monte Carlo simulations; Investigate cellular automata; Find minima and maxima, using hill climbing and simulated annealing; Try selection methods, including ...
Beginning Machine Learning in iOS
Implement machine learning models in your iOS applications. This short work begins by reviewing the primary principals of machine learning and then moves on to discussing more advanced topics, such as CoreML, the framework used to enable machine learning tasks in Apple products.
Many applications on iPhone use machine learning: Siri to serve voice-based requests, the Photos app for facial recognition, and Facebook to suggest which people that might be in a photo. You'll review how these types of machine learning tasks are implemented and performed so that you can use them in your own apps.
Beginning Machine Learning in iOS is your guide to putting machine learning to work in your iOS applications.
Understand the CoreML components; Train custom models; Implement GPU processing for better computation efficiency; Enable machine learning in your application. ...
Advanced R Statistical Programming and Data Models
Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study.
Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You'll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. This is a must-have guide and reference on using and programming with the R language.
Conduct advanced analyses in R including: generalized linear models, generalized additive models, mixed effects models, machine learning, and parallel processing; Carry out regression modeling using R data ...
Machine Learning and AI for Healthcare
Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges.
You'll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You'll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization.
Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things.
Gain a deeper understanding of key machine learning algorithms and their use and implementation within wide ...
MATLAB Machine Learning Recipes, 2nd Edition
Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more.
How to write code for machine learning, adaptive control and estimation using MATLAB; How these three areas complement each other; How these three areas are needed for robust machine learning applications; How to use MATLAB graphics and visualization tools for machine learning; How to code real world examples in MATLAB for major applications of ...
Natural Language Processing Recipes
Implement natural language processing applications with Python using a problem-solution approach. This book has numerous coding exercises that will help you to quickly deploy natural language processing techniques, such as text classification, parts of speech identification, topic modeling, text summarization, text generation, entity extraction, and sentiment analysis.
Natural Language Processing Recipes starts by offering solutions for cleaning and preprocessing text data and ways to analyze it with advanced algorithms. You'll see practical applications of the semantic as well as syntactic analysis of text, as well as complex natural language processing approaches that involve text normalization, advanced preprocessing, POS tagging, and sentiment analysis. You will also learn various applications of machine learning and deep learning in natural language processing.
By using the recipes in this book, you will have a toolbox of solutions to apply to your own projects in the real w ...