Java or Python in AI?

I compare the two below. Choose the best option for your Fintech business, and remember to vote - react with 👍 for Java and 💡 for Python.

The trick with Java vs. Python battle is that it's relevant only in the individual IT business context. Each language is fit for AI projects, but the functionalities of the two differ.

Let’s start with Python and its benefits for AI programming.

🔹 𝐇𝐢𝐠𝐡𝐥𝐲 𝐢𝐧𝐭𝐮𝐢𝐭𝐢𝐯𝐞. Python is easy to read and learn with its concise syntax and excellent usability. Those with no programming background, like data analysts, can quickly get through the Python basics to work with your AI project.

🔹 𝐄𝐚𝐬𝐲 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧. Rich in open-source libraries and frameworks, Python has a lot for creating ML algorithms. TensorFlow by Google is one of the best ones to use.

🔹 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐞𝐝 𝐚𝐥𝐥 𝐚𝐫𝐨𝐮𝐧𝐝. Simple googling will take you to tons of tutorials and bootcamps dedicated to Python. 

🔹 𝐓𝐨𝐩-𝐧𝐨𝐭𝐜𝐡 𝐯𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐭𝐨𝐨𝐥𝐬. I already mentioned the vast array of libraries Python can boast of. Visualization libraries are crucial to AI development, and this language has much to offer for presenting your data in the most compelling and understandable way.

🔹 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦-𝐢𝐧𝐝𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐭. Python is one of the languages you can run on numerous platforms without compiling it manually, as it’s an interpreted language. 

🔸 𝐒𝐥𝐨𝐰 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 is where Python falls behind Java - exactly because it’s an interpreted language. However, if the speed of development matters to you more than the performance speed, this drawback might be a minor one.

Java has quite a bunch of benefits and shares many with Python. Let’s start with them.

🔹 𝐄𝐚𝐬𝐲 𝐭𝐨 𝐮𝐬𝐞. While Java can take some extra code to write compared to other languages, it’s no rocket science, has a clear syntax, and is excellent for coding AI algorithms.

🔹 𝐒𝐩𝐞𝐞𝐝𝐲. Statistically typed and compiled, Java has faster execution than Python.

🔹 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦-𝐢𝐧𝐝𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐭. Code it once and run it on any platform. 

🔹 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐞𝐝 𝐚𝐥𝐥 𝐚𝐫𝐨𝐮𝐧𝐝. Just like Python, it has numerous libraries and tutorials. For AI-related purposes, there are also frameworks like Apache Jena (for expert systems) or Neuroph (for neural networks) for AI-related purposes.

🔹 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞. Java is fit for starting large-scale AI projects and growing the existing ones.

🔹 𝐇𝐚𝐬 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 𝐬𝐮𝐩𝐩𝐨𝐫𝐭. Having been around for a long time, the Java community timely fixes the bugs and ensures that the language evolves the fastest possible.

🔸 𝐕𝐞𝐫𝐛𝐨𝐬𝐞. The need to define classes and methods will result in more lines of code compared to Python.