Is it wrong to pick up java for Artificial intelligence?

Hey, What is going on? Hope so all fine. I have been searching for long in the field of Artificial intelligence and machine learning. Every day only one thing comes in my mind that wherever I search for Artificial Intelligence or machine learning (or deep learning) Why people tell only Python is best? Why not Java?. All right Python is best, but what if you want to start with Java or any other programming languages? Is it over? No obviously not, that’s why I decided to write an article for you to bring some knowledge or idea about why to start with Java, what things you need to start? and how to do? etc. So, read the whole article and then I hope you will get your answers over Here.

Why start with java?

You are thinking that why should I start with Java right? most of the persons have the same question because Python is in trending. They usually saw python many times in videos or articles related to AI but, it's OK, this is actually happening with me also. All right, so let's talk a little bit about Java.

Java is preferred for so many reasons like:

1. Java virtual machine technology
2. Versatile
3. Portability
4. In-built garbage collection
5. Scalability
6. Transparency
7. Maintainability
8. Wide support
9. Easy to debug

After reading the above advantages you know why to start with java. Now let's talk about how to start with it and how many libraries over there.

How to start with Java?

Working in any kind of project the main part is external support. So, I am going to tell you about libraries used for Artificial Intelligence and Machine Learning with Java but, there are some prerequisites to start learning AI with those libraries. So, If you are a beginner then you should read our article How to get started with AI? to get some knowledge about it.

Top AI learning libraries in java

1. Weka:-
Weka is the most popular library for machine learning. In the prior version of weka, it was not for java but, from the 3rd version, it comes for java which is called Weka 3. It is used for data analysis data mining and predictive modeling etc. Weka is best to work for machine learning algorithms and you can apply Weka's machine learning algorithm directly to your code.

2. Apache OpenNLP:-
Apache OpenNLP is a machine learning library for processing of natural language text.

3. Massive Online Analysis(MOA):-
MOA is the big open source software used for machine learning. It is easy to use with Weka. It is used for machine learning on data streams in real time and it is useful for IOT also.

4. Jenetics:-
Genetic algorithm, if you are interested in the genetic algorithm then the jenetics library is for you. For, genetic algorithm ECJ 23, Java genetic algorithms package and watchmaker framework are also good libraries.

5. Spring Roo:-
It is a lightweight tool and best for automatic programming. There is also an open source code generator available which is known as Accelo.

ELKI is used for data mining.
MALLET is also used in machine learning.
Another is Deeplearning4j which is used in deep learning.

These are the top libraries used in machine learning and deep learning for Java and even so many libraries are available for Java. So, you can easily do research, building projects and learning with the help of these libraries which are for Java. Which library attracted you most? Let me know down below in the comment section.

Technology grows extremely fast, you have to stay with it. In the future, you will see artificial intelligence, machine learning everywhere. So, don’t you think you should have to contribute to them? now, what are you waiting for?. Just wake up and choose your programming language for AI(or ML) whether it is Python or Java.

Whenever the word comes Artificial intelligence then the name of only(80%) one programming language comes and that is “Python”. So, I wrote this article, just to memorize that other language are not dead. If you don’t know Python then OK, start with Java(or your favorite programming language). Java is also good as compared to Python. Both are good as per their own pros and cons. I hope you like this article, this is just an overview. You can get more deep knowledge here. So, stay with us and don’t forget to share this article. 

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  1. Deep Learning is all the rage. DL4J is far behind many others such as PyTorch or Chainer. The reason why python works so well is because of it's reference counting, once an object is no longer used it is immediately collected and frees up the memory in the GPU, of which, the memory is very precious. In Java, it's just not very suitable for this type of thing because of it's garbage collector. A shame, because otherwise it's a great and efficient language. If Java had a reference counted implementation it would be IMO far better and more efficient for deep learning.

    1. Java has an abstract class called Reference with a few sub-types that allows you to garbage collect objects not being referenced in a program. and each subtype provides a different level of indirection to the garbage collector.

    2. Weak references and the like unfortunately provide no help in eagerly collecting garbage.