Daily Dose for 2017.10.16

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Xamarin Blueprints
Pivotal Certified Professional Spring Developer Exam
Natural Language Processing in Action
Reactive Machine Learning Systems
Real-World Machine Learning

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Books/Videos on Sale (or Free) Today

These deals are good for today only, so be sure to take advantage of the pricing before the offers expire.

Free Xamarin Blueprints

Xamarin Blueprints Free Packt eBook by Michael Williams (valid through 10/16 at 19:00 EST). This book covers Xamarin, Mobile Development, iOS Development, Android Development.

Publisher’s Description

Do you want to create powerful, efficient, and independent apps from scratch that will leverage the Xamarin framework and code with C#? Well, look no further; you’ve come to the right place!

This is a learn-as-you-build practical guide to building eight full-fledged applications using Xamarin.Forms, Xamarin Android, and Xamarin iOS.

Each chapter includes a project, takes you through the process of building applications (such as a gallery Application, a text-to-speech service app, a GPS locator app, and a stock market app), and will show you how to deploy the application’s source code to a Google Cloud Source Repository. Other practical projects include a chat and a media-editing app, as well as other examples fit to adorn any developer’s utility belt.

In the course of building applications, this book will teach you how to design and prototype professional-grade applications implementing performance and security considerations.

What You Will Learn

  • Discover eight different ways to create your own Xamarin applications
  • Improve app performance by using SQLite for data-intensive applications
  • Set up a simple web service to feed JSON data into mobile applications
  • Store files locally with Xamarin.Forms using dependency services
  • Use Xamarin extension libraries to create effective applications with less coding

About the Author

Michael Williams is an Insightful, results-driven full stack developer with notable experience in cross-platform development using Xamarin and native languages for multiple platforms. He also builds and researches server-side architecture using CQRS and event-sourcing.

Also an entrepreneur, the owner of Flush Arcade, a company involved in developing creative, innovative, and ideative games (flusharcade.com.au).

$9.99 Pivotal Certified Professional Spring Developer Exam

Pivotal Certified Professional Spring Developer Exam $9.99 Apress eBook by Iuliana Cosmina. This book covers Pivotal Certified Professional, Spring Framework.

Publisher’s Description

Will help you master the core principles of the popular Spring Framework Will help you prepare for the Pivotal Certified Professional exam Includes mock tests at the end of each chapter, with answers included in an appendix

Pass the Pivotal Certified Professional exam using source code examples, study summaries, and mock exams. In this book, you’ll find a descriptive overview of certification-related Spring modules and a single example application demonstrating the use of all required Spring modules. Also, it is suitable as an introductory primer for Spring newcomers.

Furthermore, in Pivotal Certified Professional Spring Developer Exam: A Study Guide each chapter contains a brief study summary and question set, and the book’s free downloadable source code package includes one mock exam (50 questions – like a real exam). After using this study guide, you will be ready to take and pass the Pivotal Certified Professional exam.

When you become Pivotal Certified, you will have one of the most valuable credentials in Java. The demand for Spring skills is skyrocketing. Pivotal certification helps you advance your skills and your career, and get the maximum benefit from Spring. Passing the exam demonstrates your understanding of Spring and validates your familiarity with: container-basics, aspect oriented programming (AOP), data access and transactions, Spring Security, Spring Boot, microservices and the Spring model-view-controller (MVC). Good luck!

What You’ll Learn

  • Understand the core principles of the popular Spring Framework
  • Use dependency injection
  • Work with aspects in Spring and do AOP (aspect oriented programming)
  • Control transactional behavior and work with SQL and NoSQL (MongoDB) databases
  • Create and secure web applications based on Spring MVC
  • Get to know the format of exam and type of questions in it
  • Create Spring microservices applications

Who This Book Is For

Spring developers who have taken the Pivotal Core Spring class are eligible to take the Pivotal Certified Professional exam.

About the Author

Iuliana Cosmina is a software engineer and professional developer. She has been programming in Java for more than 10 years. She also taught Java at the Gheorge Asachi Technical University in Iasi, Romania. She has a Bachelor’s degree in computer science and a Master’s degree in distributed systems from the same university.

She discovered Spring in June 2012 and loved it so much she trained for and passed the exam to become a Certified Spring Professional in November 2012. She trained for and passed the exam to become a Certified Web Application Developer in May 2014.

Her plan is to become a Spring Enterprise Integration Specialist in the near future.

She has contributed to the development of different types of enterprise applications such as search engines, ERPs, track and trace, and banking. During her career in outsourcing she has been a team leader, acting software architect and a DevOps professional. She likes to share her knowledge and expertise via tutoring, teaching, and mentoring. She lives in Sibiu, Romania and works as a software engineer for BearingPoint, a multinational management and technology consulting company.

When she is not programming, she spends her time reading, travelling, hiking, or biking.

50% off Natural Language Processing in Action

Natural Language Processing in Action 50% off Manning’s eBook by Hobson Lane, Hannes Hapke, Cole Howard. This book covers Natural Language Processing, Machine Learning, Chatbot, Neural Networks, Word2vec, Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory Networks.

Publisher’s Description

Natural Language Processing in Action is your guide to creating machines that understand human language. You’ll start with a mental model of how a computer learns to read and interpret language. Then, you’ll discover how to train a NLP machine to recognize patterns and extract information from text. As you explore the carefully-chosen examples, you’ll expand your machine’s knowledge and apply it to a range of challenges, from building a search engine that can find documents based on their meaning rather than merely keywords, to training a chatbot that uses deep learning to answer questions and participate in a conversation.

About the technology

Most humans are pretty good at reading and interpreting text; computers…not so much. Natural Language Processing (NLP) is the discipline of teaching computers to read more like people, and you see examples of it in everything from chatbots to the speech-recognition software on your phone. Modern NLP techniques based on machine learning radically improve the ability of software to recognize patterns, use context to infer meaning, and accurately discern intent from poorly-structured text. NLP promises to help you improve customer interactions, save cost, and reinvent text-intensive applications like search or product support.

What’s inside

  • Parsing and normalizing text
  • Rule-based (Grammar) NLP
  • Data-based (Machine Learning) NLP
  • Deep Learning NLP
  • End-to-end chatbot pipeline with training data
  • Scalable NLP pipelines
  • Hyperparameter optimization algorithms

About the reader

A basic understanding of machine learning and some experience with a modern programming language such as Python, Java, C++, or JavaScript will be helpful.

About the authors

Hobson Lane has more than 15 years of experience building autonomous systems that make important decisions on behalf of humans.

Hannes Hapke is an Electrical Engineer turned Data Scientist with experience in deep learning.

Cole Howard is a carpenter and writer turned Deep Learning expert.

50% off Reactive Machine Learning Systems

Reactive Machine Learning Systems 50% off Manning’s eBook by Jeff Smith. This book covers Machine Learning, Reactive Design, Scala, Spark, MLlib, Akka, Big Data, Functional Programming, Microservices, Bayesian Modeling, Naive Bayes.

Publisher’s Description

Reactive Machine Learning Systems teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app. This example-rich guide starts with an overview of machine learning systems while focusing on where reactive design fits. Then you’ll discover how to develop design patterns to implement and coordinate ML subsystems. Using Scala and powerful frameworks such as Spark, MLlib, and Akka, you’ll learn to quickly and reliably move from a single machine to a massive cluster. Finally, you’ll see how you can operate a large-scale machine learning system over time. By the end, you’ll be employing the principles of reactive systems design to build machine learning applications that are responsive, resilient, and elastic.

About the technology

Machine learning applications autonomously reason about data at massive scale. It’s important that they remain responsive in the face of failure and changes in load. And the best way to to keep applications responsive, resilient, and elastic is to incorporate reactive design. But machine learning systems are different than other applications when it comes to testing, building, deploying, and monitoring. They also have unique challenges when you need to change the semantics or architecture of the system. To make machine learning systems reactive, you need to understand both reactive design patterns and modern data architecture patterns.

What’s inside

  • Functional programming for distributed systems
  • Reactive techniques like futures, actors, and supervision
  • Spark and MLlib, and Akka
  • Scala-based examples
  • Predictive microservices
  • Data models for uncertain data
  • Design patterns for machine learning systems

About the reader

Readers should have intermediate skills in Java or Scala. No previous machine learning experience is required.

About the author

Jeff Smith builds large-scale machine learning systems using Scala and Spark. For the past decade, he has been working on data science applications at various startups in New York, San Francisco, and Hong Kong. He blogs and speaks about various aspects of building real world machine learning systems.

50% off Real-World Machine Learning

Real-World Machine Learning 50% off Manning’s eBook by Henrik Brink, Joseph W. Richards, Mark Fetherolf with foreword by Beau Cronin. This book covers Machine Learning, Big Data, Python, Natural Language Processing, Naive Bayes, TF-IDF, Word2vec.

Publisher’s Description

Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.

About the technology

Machine learning systems help you find valuable insights and patterns in data, which you’d never recognize with traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior, and make fact-based recommendations. It’s a hot and growing field, and up-to-speed ML developers are in demand.

About the book

Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you’ll build skills in data acquisition and modeling, classification, and regression. You’ll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you’re done, you’ll be ready to successfully build, deploy, and maintain your own powerful ML systems.

What’s inside

  • Predicting future behavior
  • Performance evaluation and optimization
  • Analyzing sentiment and making recommendations

About the reader

No prior machine learning experience assumed. Readers should know Python.

About the authors

Henrik Brink, Joseph Richards and Mark Fetherolf are experienced data scientists engaged in the daily practice of machine learning.

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