You talk to any Software developer and he will agree that right now machine learning is the hottest and latest trends in software development market. Researchers believe that Machine Learning is going to totally transform the web development process of many types, including web and mobile applications development.

There has been a powerful impact of Machine Learning (ML) on web development. It is a good alternative to conventional data mining. ML also removes the security threats. There is a huge collection of Machine Learning APIs available on internet. ML helps in speeding up product delivery and also helps in producing customized content and information. Using ML, the businesses can better understand the customer behavior and their patterns. ML uses some algorithms to make computers learn without being separately programmed. It is the best method of data analysis which also automates the creation of analytical models. These are the reasons why ML frameworks play an important role in the web development.

In this article, we will discuss about some of the major Machine Learning Frameworks for web development. Let’s start now:-

Best Machine Learning Frameworks

TensorFlow

If you want to implement ML in your project, then you need to Hire Software Developers with expertise in ML and you can use most popular TensorFlow ML framework. TensorFlow is written in Python, Java and Go and it is one of the most popular machine learning frameworks for Java development. Basically it is an open source library that uses data flow graphs for numerical computation. TensorFlow is the most popular and widespread machine learning project on GitHub and has taxpayers as the largest participants. TensorFlow was originally developed by researchers and engineers from the Google Brain team within Google’s AI organization. That’s why it comes with strong support for machine learning and deep learning and its flexible numerical computation core is used across many other scientific domains. The flexible architecture of TensorFlow allows users to easily implement computations on one or more GPUs or CPUs with a single API, irrespective of whether it is a desktop computer, a server or even a mobile phone. The nodes in the graph represent mathematical operations, while the edges of the graph represent the multidimensional data sets i.e. tensors which communicates between them.

Microsoft Cognitive Toolkit

This particular ML Toolkit by Microsoft is an open source deep learning toolkit, developed in Python and C++ and used for training algorithms to learn like a human brain. By using this ML tool, you can use various machine learning models like feed-forward DNNs, Convolutional neural networks and recurrent neural networks. This tool uses neural networks to go through large unstructured datasets. It needs lesser training time and it has easy to use architecture and also it is highly customizable, allowing you to choose your own parameters, networks and algorithms. It is supported for multi-machine-multi-GPS backends and it is better than many of its competitors.

Caffe 

When you need to include ML in your web project then talk to your Web Developer that whether he has expertise in ML implementation or not. You can use Caffe ML framework as it offers many benefits. Caffe machine learning framework is created using C++ and Python. It is a deep learning framework for Java Development especially made for high speed, modularity and expression. It has been developed by the Berkley AI Research and development team. Its expressive architecture motivates personalized application and more innovation. Its configuration options enable users to easily switch between CPU and GPU by doing setting for a single indicator. Caffe has an extensible code which helps in fueling its early growth, making it another highly successful GitHub machine learning project. This framework is highly fast therefore it is valuable for research institutions and industrial implementations and executions of projects. It can be used for image classification and computer vision by taking benefit of convolutional neural networks. It also offers model Zoo which is a set of pre-trained models which do not require any coding to implement it.

Apache Singa

Apache Sing ML framework has been written in C++, Python and Java. It is a highly scalable and flexible deep learning platform used for big data analytic projects. It is developed by the team of the National University of Singapore. This machine learning framework offers a flexible architecture for scalable distributed training in large volumes of research data. It is extensible and can run on wide range of hardware. Its main applications are in natural language processing (NLP), image recognition and in image processing. Right now an Apache incubator project offers a simple programming model which can work in a group of nodes. In it, deep distributed learning uses model sharing and parallelization during the period of training. Apache Singa supports traditional machine learning models like logistic regression etc.

Apache Mahout

This particular ML framework has been created using Java and Scala and it is one of the most popular open source framework from Apache which is mainly designed for data scientists, statisticians and mathematicians to help them in quickly executing their own algorithms. Apache Mahout is a distributed linear Algebra framework for creating machine learning applications with scalable performances. Generally, Mahout works on collaborative grouping, filtering and classifications. It also provides you with ability to develop your own mathematical calculations in a highly interactive environment which actually runs on a big data platform and after that move the same code into your application and executes it. This framework also offers distributed linear algebra and an engine of statistics which is working and is it is distributed together with an interactive shell and the library to connect to this application which is in production. Also it reaches onto the Apache Hadoop platform using the map paradigm, but it doesn’t stop contributions to other implementations which are based on Hadoop.

Conclusion

We have discussed about the best and most popular machine learning frameworks available in the market especially which are developed using Java as primary language. It looks like, web development with machine learning is going to revolutionize the IT industry. Python is also playing its role in the development of these frameworks. Various other popular machine learning frameworks and libraries are written in or supported by Python which includes Theano, TensorFlow, Keras and many smaller projects like Neon, Veles, Chainer and Microsoft Azure Studio to name a few. If you too are looking to start your next web development project and want to implement machine learning, then you too can use any of these machine learning frameworks as per your requirements.