Currently, machine learning is one of the hottest trends in software development. Many analysts even believe that machine learning is going to completely transform the web development process of several programs, including web and mobile applications. Machine learning frameworks
The following points clearly indicate the powerful impact of machine learning on web development:
–> Good alternative to conventional data mining
–> Removes security threats
–> The stock of machine learning APIs
–> Speedup Product Discovery
–> Produce customized content and information
–> Understand Customer Behaviour
Machine learning uses some algorithms to make computers learn without being explicitly programmed. It is the finest method of data analysis which automates the creation of analytical models. That’s why machine learning frameworks play an important role in web development.
In this blog, we will discuss some major machine learning frameworks for web development. The list is given below:
Top Machine Learning Frameworks
1) Microsoft Cognitive Toolkit
Written in: Python and C++
This is an open source deep learning toolkit by Microsoft used for training algorithms to learn like a human brain. By using this tool, you can utilize various machine learning models like convolutional neural networks, feed-forwards DNNs, and recurrent neural networks.
Undoubtedly, this tool is designed to use neural networks to go through large unstructured datasets. With faster training times and easy-to-use architecture, it is highly customizable, allowing you to choose your own parameters, networks, and algorithms. A special thanks to its support for multi-machine-multi-GPU backends, it can easily surpass many of its competitors.
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Try it here: Microsoft
2) TensorFlow
Written in: Python, Java and Go
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It is one of the most popular machine learning frameworks for Java development. It is an opensource library that uses data flow graphs for numerical computation. No doubt, TensorFlow is the most bifurcated machine learning project on GitHub and also has the largest participation of taxpayers.
The flexible architecture of TensorFlow makes it easy for users to implement computations on one or more GPUs or CPUs with a single API, regardless 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 (tensors) communicated between them.
Try it here: Tenserflow
3) Apache Mahout
Written in: Java and Scala
It is another most popular open source offering from Apache which is mainly designed for statisticians, data scientists, and mathematicians so that they can quickly execute their own algorithms. Moreover, it is a distributed linear algebra framework for creating machine learning applications with scalable performance. Mahout focuses mainly on collaborative grouping, filtering, and classification.
In addition, it gives you the ability to develop your own mathematical calculations in an interactive environment that actually runs on a big data platform, then move exactly the same code into your application and implement.
Mahout Samsara also provides a distributed linear algebra and an engine of statistics that is working and is distributed together with an interactive shell and the library to link to its application in production. It often climbs onto the Apache Hadoop platform using the map / reduce paradigm, but this does not restrict contributions to other implementations based on Hadoop.
Try it here: Github
4) Caffe
Written in: C++ and Python
It is a deep learning framework for Java development especially made for speed, expression, and modularity. It is developed by the Berkley AI Research team. Expressive architecture encourages personalized application and innovation.
Moreover, the configuration options allow users to switch between GPU and CPU by configuring a single indicator. The extensible code of Caffe has helped fuel its early growth, making it another highly successful GitHub machine learning project.
The speed of Caffe makes it valuable for research institutions and industrial implementations. It was developed for image classification/computer vision by taking advantage of convolutional neural networks. It also offers the Model Zoo, which is a set of pre-trained models that do not require any coding to implement.
Anyhow, it should be noted that Caffe is the most suitable for the construction of applications and not specifically intended for anything other than artificial vision.
Try it here: Caffe
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5) Apache Singa
Written in: C++, Python, and Java.
Apache Singa is a scalable and flexible deep learning platform used for big data analytics. It was developed by the team of the National University of Singapore. This machine learning framework provides a flexible architecture for scalable distributed training in large volumes of data.
It is extensible to be running on a wide range of hardware. The main applications are in natural language processing (NLP) and image recognition.
Currently, an Apache incubator project provides a simple programming model that can work in a group of nodes. Deep distributed learning uses model sharing and parallelization during the training process.
However, Singa supports traditional machine learning models such as logistic regression.
Conclusion:
So we have seen some top machine learning frameworks for Java development. In fact, web development with machine learning is going to revolutionalize the IT world. However, the various popular Machine Learning frameworks and libraries are written in or primarily supported by Python that includes Keras, Theano, TensorFlow and smaller projects like Microsoft Azure Studio, sci-kit learn, Veles, Chainer, Neon.
So if you are curious to develop your next project with one of these machine learning frameworks, then it is the right time to start with. You can also hire skilled Golang developers or ML web developers from a reliable web development company like ValueCoders.
