Machine Vision Algorithms In Java
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Machine Vision Algorithms in Java
Author: Paul F. Whelan
language: en
Publisher: Springer Science & Business Media
Release Date: 2001
This book presents key machine vision techniques and algorithms, along with the associated Java source code. Special features include a complete self-contained treatment of all topics and techniques essential to the understanding and implementation of machine vision; an introduction to object-oriented programming and to the Java programming language, with particular reference to its imaging capabilities; Java source code for a wide range of real-world image processing and analysis functions; an introduction to the Java 2D imaging and Java Advanced Imaging (JAI) API; and a wide range of illustrative examples.
Ultimate Java for Data Analytics and Machine Learning
Author: Abhishek Kumar
language: en
Publisher: Orange Education Pvt Ltd
Release Date: 2024-08-08
TAGLINE Empower Your Data Insights with Java's Top Tools and Frameworks. KEY FEATURES ● Explore diverse techniques and algorithms for data analytics using Java. ● Learn through hands-on examples and practical applications in each chapter. ● Master essential tools and frameworks such as JFreeChart for data visualization and Deeplearning4j for deep learning. DESCRIPTION This book is a comprehensive guide to data analysis using Java. It starts with the fundamentals, covering the purpose of data analysis, different data types and structures, and how to pre-process datasets. It then introduces popular Java libraries like WEKA and Rapidminer for efficient data analysis. The middle section of the book dives deeper into statistical techniques like descriptive analysis and random sampling, along with practical skills in working with relational databases (JDBC, SQL, MySQL) and NoSQL databases. It also explores various analysis methods like regression, classification, and clustering, along with applications in business intelligence and time series prediction. The final part of the book gives a brief overview of big data analysis with Java frameworks like MapReduce, and introduces deep learning with the Deeplearning4J library. Whether you're new to data analysis or want to improve your Java skills, this book offers a step-by-step approach with real-world examples to help you master data analysis using Java. WHAT WILL YOU LEARN ● Understand foundational principles and types of data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics. ● Master techniques for preprocessing data, including cleaning and munging, to prepare it for analysis. ● Learn how to create various charts and plots including bar charts, histograms, and scatter plots for effective data visualization. ● Explore Java-based libraries such as WEKA and Deeplearning4j for implementing machine learning algorithms. ● Develop expertise in statistical techniques including hypothesis testing, regression (linear and polynomial), and probability distributions. ● Acquire practical skills in SQL querying and JDBC for relational databases. ● Explore applications in business intelligence and deep learning, including image recognition and natural language processing. WHO IS THIS BOOK FOR? This book is ideal for IT professionals, software developers, and data scientists interested in using Java for data analytics. It is also suitable for students and researchers seeking practical insights into Java-based data analysis. Readers should have a basic understanding of Java programming and fundamental concepts in data analysis. TABLE OF CONTENTS 1. Data Analytics Using Java 2. Datasets 3. Data Visualization 4. Java Machine Learning Libraries 5. Statistical Analysis 6. Relational Databases 7. Regression Analysis 8. Classification Analysis 9. Sentiment Analysis 10. Cluster Analysis 11. Working with NoSQL Databases 12. Recommender Systems 13. Applications of Data Analysis 14. Big Data Analysis with Java 15. Deep Learning with Java Index
Programming Computer Vision with Python
For readers needing a basic understanding of Computer Vision's underlying theory and algorithms, this hands-on introduction is the ideal place to start. Examples written in Python are provided with modules for handling images, mathematical computing, and data mining.