Practical Java machine learning : projects with Google Cloud platform and Amazon web services /: projects with Google Cloud platform and Amazon web services. ([2018])
- Record Type:
- Book
- Title:
- Practical Java machine learning : projects with Google Cloud platform and Amazon web services /: projects with Google Cloud platform and Amazon web services. ([2018])
- Main Title:
- Practical Java machine learning : projects with Google Cloud platform and Amazon web services
- Further Information:
- Note: Mark Wickham.
- Authors:
- Wickham, Mark
- Contents:
- 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 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 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 CreatingIntro; 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 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 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 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 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 … (more)
- Publisher Details:
- New York, NY : Apress
- Publication Date:
- 2018
- Copyright Date:
- 2018
- Extent:
- 1 online resource
- Subjects:
- 006.31
Computer science
Machine learning
Java (Computer program language)
COMPUTERS / General
Computers -- Computer Science
Computers -- Programming Languages -- General
Program concepts / learning to program
Programming & scripting languages: general
Java (Computer program language)
Electronic data processing
Computers -- Programming Languages -- Java
Electronic books - Languages:
- English
- ISBNs:
- 9781484239513
1484239512 - Related ISBNs:
- 9781484239506
- Notes:
- Note: Online resource; title from PDF title page (EBSCO, viewed October 29, 2018).
- Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.346782
- Ingest File:
- 01_300.xml