To be honest, when working with multiple languages, there is a chance that you will mix a few concepts, so you must concentrate on one thing, and that one thing must be good and best, So today we’ll look at which programming languages are good, easy to learn, and have an echo-system. All these things make a programming language more powerful; this is an echo system.
For Python, there are a couple of frameworks, and with these frameworks, we can extend the power of Python not just for desktop applications and machine learning but also for web development as well. The major frameworks are Flask and Django. Both of these frameworks are good, and using them we can deploy and display different machine learning models.
Examples of Python: Python is one of the official server-side languages used by Google; it is also used inside Instagram and Facebook infrastructures. Also, Quora, Netflix, and Spotify use it extensively, mainly to power their data analysis capabilities and backend services.
A popular open-source library that enables the deployment of machine learning programs in the browser is TensorFlow.js. It allows running existing models in the browser, training ready models with your own data, and developing new machine learning models directly in the browser. Another commonly used library is Brain, which enables the creation and training of neural networks and loading them onto a browser, e.g., to recognize color contrast.
Other prominent examples of using this object-oriented programming language in ML are as follows:
An educational web app that lets you play with neural networks and learn about their various components. It has a nice user interface that allows you to control the inputs, the number of neurons, which algorithm to use, and various other metrics that will be reflected in the end result. There’s also a lot to learn from the app behind the scenes — the code is open source and uses a custom machine learning library written in TypeScript and well-documented.
Probably the most actively supported project on this list, Synaptic is an architecture agnostic Node.js and browser library, allowing developers to build whatever type of neural networks they want. It has several built-in architectures, allowing you to quickly test and compare different machine learning algorithms. It also has a well-written introduction to neural networks, a number of hands-on demos, and many other great tutorials that uncover myths about how machine learning works.
Land Lines is an interesting Chrome web experiment that finds satellite images of the Earth similar to those drawn by the user. The application does not talk to the server: it runs entirely in a browser and, thanks to the clever use of machine learning and WebGL, has excellent performance even on mobile devices. You can check out the source code on GitHub or read the full example here.
Thing Translator is a web experiment that allows your phone to recognize real objects and give them names in different languages. The app is completely web-based and uses two machine learning APIs from Google — Cloud Vision for image recognition and Translate API for natural language translations.
DeepForge is a user-friendly development environment for working with deep learning. It allows you to build neural networks with a simple GUI, supports training models on remote machines, and has built-in version control. The project runs in a browser and is based on Node.js and MongoDB, making the installation process familiar to most web developers.
AI and machine learning development are fast-progressing, and there’s no shortage of their innovative applications in business. Some prominent examples include image classifiers, social media sentiment analytics, chatbots, predictive engines, or personalized recommendations (look here for more examples). To implement these tools and functionalities, data scientists, programmers and DevOps use a combination of programming languages, Python included.
So, is Python the best language for machine learning? For years, Python has been the language of choice for ML implementations. It provides a comprehensive library of packages with in-built functions that facilitate data analysis and processing, cleansing, modeling, visualization, and so on. These include TensorFlow, Keras, and Theano, all of which make it easy to implement various machine learning features. Many developers consider Python the preferred language for ML projects also because of its capability of interacting with other languages and platforms, and robust data handling capacity.
From a business standpoint, Python is used for machine learning projects for several reasons. First of all, it’s highly productive thanks to its design and has a ton of ready to use packages, which positively impacts the speed of implementation. Secondly, Python has a large community of developers and supporters. It’s estimated that there are more than 8 million Python coders around the world. This makes it easier to find people with the right skills to launch the project quickly. The extensive community support also enables daily code enhancements and regular creation of new libraries and packages that further accelerate the pace of development.
For me, Python is the key, because of its industry and flexibility. Python is my preferred programming language because of its large package library.