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Practical Java machine learning : projects with Google Cloud platform and Amazon web services /
Build machine learning (ML) solutions for Java development. This book shows you that when designing ML apps, data is the key driver and must be considered throughout all phases of the project life cycle. Practical Java Machine Learning helps you understand the importance of data and how to organize...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Apress,
[2018]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Wickham, Mark, |e author. | |
| 245 | 1 | 0 | |a Practical Java machine learning : |b projects with Google Cloud platform and Amazon web services / |c Mark Wickham. |
| 264 | 1 | |a New York, NY : |b Apress, |c [2018] | |
| 264 | 4 | |c Ã2018 | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 347 | |a text file | ||
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| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed October 29, 2018). | |
| 505 | 0 | |a Intro; Table of Contents; About the Author; About the Technical Reviewer; Preface; Chapter 1: Introduction; 1.1 Terminology; 1.2 Historical; 1.3 Machine Learning Business Case; Machine Learning Hype; Challenges and Concerns; Data Science Platforms; ML Monetization; The Case for Classic Machine Learning on Mobile; 1.4 Deep Learning; Identifying DL Applications; 1.5 ML-Gates Methodology; ML-Gate 6: Identify the Well-Defined Problem; ML-Gate 5: Acquire Sufficient Data; ML-Gate 4: Process/Clean/Visualize the Data; ML-Gate 3: Generate a Model; ML-Gate 2: Test/Refine the Model. | |
| 505 | 8 | |a ML-Gate 1: Integrate the ModelML-Gate 0: Deployment; Methodology Summary; 1.6 The Case for Java; Java Market; Java Versions; Installing Java; Java Performance; 1.7 Development Environments; Android Studio; Eclipse; Net Beans IDE; 1.8 Competitive Advantage; Standing on the Shoulders of Giants; Bridging Domains; 1.9 Chapter Summary; Key Findings; Chapter 2: Data: The Fuel for Machine Learning; 2.1 Megatrends; Explosion of Data; Highly Scalable Computing Resources; Advancement in Algorithms; 2.2 Think Like a Data Scientist; Data Nomenclature; Defining Data; 2.3 Data Formats. | |
| 505 | 8 | |a CSV Files and Apache OpenOfficeARFF Files; JSON; 2.4 JSON Integration; JSON with Android SDK; JSON with Java JDK; 2.5 Data Preprocessing; Instances, Attributes, Labels, and Features; Data Type Identification; Missing Values and Duplicates; Erroneous Values and Outliers; Macro Processing with OpenOffice Calc; JSON Validation; 2.6 Creating Your Own Data; Wifi Gathering; 2.7 Visualization; JavaScript Visualization Libraries; D3 Plus; 2.8 Project: D3 Visualization; 2.9 Project: Android Data Visualization; 2.10 Summary; Key Data Findings; Chapter 3: Leveraging Cloud Platforms; 3.1 Introduction. | |
| 505 | 8 | |a Commercial Cloud ProvidersCompetitive Positioning; Pricing; 3.2 Google Cloud Platform (GCP); Google Compute Engine (GCE) Virtual Machines (VM); Google Cloud SDK; Google Cloud Client Libraries; Cloud Tools for Eclipse (CT4E); GCP Cloud Machine Learning Engine (ML Engine); GCP Free Tier Pricing Details; 3.3 Amazon AWS; AWS Machine Learning; AWS ML Building and Deploying Models; AWS EC2 AMI; Running Weka ML in the AWS Cloud; AWS SageMaker; AWS SDK for Java; AWS Free Tier Pricing Details; 3.4 Machine Learning APIs; Using ML REST APIs; Alternative ML API Providers. | |
| 505 | 8 | |a 3.5 Project: GCP Cloud Speech API for AndroidCloud Speech API App Overview; GCP Machine Learning APIs; Cloud Speech API Authentication; Android Audio; Cloud Speech API App Summary; 3.6 Cloud Data for Machine Learning; Unstructured Data; NoSQL Databases; NoSQL Data Store Methods; Apache Cassandra Java Interface; 3.7 Cloud Platform Summary; Chapter 4: Algorithms: The Brains of Machine Learning; 4.1 Introduction; ML-Gate 3; 4.2 Algorithm Styles; Labeled vs. Unlabeled Data; 4.3 Supervised Learning; 4.4 Unsupervised Learning; 4.5 Semi-Supervised Learning; 4.6 Alternative Learning Styles. | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a Build machine learning (ML) solutions for Java development. This book shows you that when designing ML apps, data is the key driver and must be considered throughout all phases of the project life cycle. Practical Java Machine Learning helps you understand the importance of data and how to organize it for use within your ML project. You will be introduced to tools which can help you identify and manage your data including JSON, visualization, NoSQL databases, and cloud platforms including Google Cloud Platform and Amazon Web Services. Practical Java Machine Learning includes multiple projects, with particular focus on the Android mobile platform and features such as sensors, camera, and connectivity, each of which produce data that can power unique machine learning solutions. You will learn to build a variety of applications that demonstrate the capabilities of the Google Cloud Platform machine learning API, including data visualization for Java; document classification using the Weka ML environment; audio file classification for Android using ML with spectrogram voice data; and machine learning using device sensor data. After reading this book, you will come away with case study examples and projects that you can take away as templates for re-use and exploration for your own machine learning programming projects with Java. You will: Identify, organize, and architect the data required for ML projects Deploy ML solutions in conjunction with cloud providers such as Google and Amazon Determine which algorithm is the most appropriate for a specific ML problem Implement Java ML solutions on Android mobile devices Create Java ML solutions to work with sensor data Build Java streaming based solutions. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Java (Computer program language) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Java (Langage de programmation) | |
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| 650 | 7 | |a Machine learning |2 fast | |
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