Java Using Machine Learning
Bright Future of Machine Learning ďƒ˜ If you have good experience in Java programming, you must need to get move of this curve now when latest innovative consulting are staring to seriously invest in machine learning. ďƒ˜ When you startup today, you can increase development skill on few some years but must start somewhere.
Java Libraries For Implementing ML • ADAMS - Advanced Data mining And Machine learning • Deeplearning4j - Deep Learning for Java DL4J
• ELKI • JavaML - Java Machine Learning
JavaML
• JSAT - Java Statistical Analysis Tool
Java Libraries For Implementing ML • Mahout - Apache Mahout • MALLET - Java "Machine Learning for Language Toolkit • MOA - Massive Online Analysis
• RapidMiner • Weka - Waikato Environment for Knowledge Analysis
Why Use Java for ML? • Number of Data is an essential to developing machine learning services, Machine learning tools require to integrate well with those technologies. Machine learning starts with collecting data.
• That’s why machine learning driving you select is critical. The correct tools solve a lot of integration issues and they will boost the digital world for businesses.
Machine Learning Process Training Data
Algorithm
Validation Data
Ho(X) Study
Evaluate
Quality Metrics
Choosing The best Ho(X)
Resource by Javaworld.com
Unlabeled Data
Predict
Predict Label Y
Top 5 Programming Languages For ML
Machine Learning Python
Java
R
Prolog Lisp
ML Frameworks for Java Apache Singa is a portable and scalable machine learning technology for big data. This deep learning framework gets a portable architecture for extensible provided training on large scale data. It writing code into a Java application development.
Apache Mahout provides you the facility to define your own calculating in a responsive way that mostly explores on a big data platform then comes entirely similar code into your app. Apache Singa same as written code in Java.
TensorFlow’s custom architecture creates it simply for users to build math top number of CPUs with an also connect single API, It doesn’t matter to connect number of CPU with different devices.
Advantages of Machine learning • As machine learning use different field such as retail, banking, medical and many other industries. • Now many reputed brands are using machine learning to push relevant promotions. • Machine learning in mostly used multiple data used in custom architecture
• It provides project deliver time reduce and better way utilize specific resources. • It easily resolve complex process environments
Machine learning Example
Input
Feature Extraction
Classification
Output
Machine learning Fact It mainly goal to build machine learning algorithm on statistical syllabus vector, matrix and other basics.
It is often more useful than your chosen Java programming language. It provide high quality library and framework widely used in ML.
It helps when developing huge function & method. Java and Python have better benefits of exact output.
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