Machine Learning is currently a hot trend in software development. Many analysts believe Machine Learning will revolutionize the web development process for many programs, including mobile apps.
These points show the profound impact of Machine Learning in web development:
- Alternative to traditional data mining
- Security threats removed
- Machine Learning APIs in stock
- Speedup Product Discovery
- Create custom content and information
- Understanding Customer Behavior
Machine Learning is a method that allows computers to learn by using certain algorithms without having to be programmed. This is the best method for data analysis, automating the creation of analytic models. Machine Learning frameworks are an essential part of web development.
This article will cover the major Machine Learning frameworks that are used for web development. Below is a list of Machine Learning frameworks for web development:
Top 5 Machine Learning Frameworks
1) Microsoft Cognitive Toolkit
Written in Python, and C++
This open-source Deep Learning toolkit from Microsoft is used to train algorithms to learn like a human’s brain. This tool allows you to use various Machine Learning models such as convolutional neural networks, feed-forward DNNs, and recurrent neural networks.
This tool uses neural networks to process large, unstructured data sets. It is easy to use and has a faster training time. A special thanks to its support for multi-machine-multi-GPU backends, it can easily surpass many of its competitors.
2) Apache Singa
Written by: C++ and Python.
Apache Singa, a flexible and scalable Deep Learning platform for big data analysis, is available. It was created by the National University of Singapore. This Machine Learning framework is flexible and allows for distributed, scalable training with large amounts of data.
It can be run on many different hardware types and is therefore extensible. It is used primarily for image recognition and natural language processing (NLP).
An Apache incubator project currently provides a simple programming language that can be used in a group. Deep distributed learning is achieved through model sharing and parallelization.
Singa however supports traditional Machine Learning models like logistic regression.
You can try it here: Docker or AWS
3) Caffe
Written in C++, and Python
It is a Deep Learning framework Java development that has been specifically designed for speed, expression, and modularity. The Berkeley AI Research team developed it. Expression architecture promotes personal application and innovation.
The configuration options enable users to switch between CPU and GPU by simply configuring one indicator. Caffe’s extensible code has been a key factor in its rapid growth and made it another successful GitHub Machine Learning project.
Caffe’s speed makes it valuable both for industrial applications and research institutions. It was developed for image classification/computer vision by taking advantage of convolutional neural networks. The Model Zoo is also available. This allows you to use pre-trained models without any programming.
Caffe can be used to build applications, but it is not intended for artificial vision.
4) Apache Mahout
Written in Java, Scala
This is the Apache Open-Source offering that is most widely used. It is designed for data scientists, mathematicians, statisticians, and other mathematical professionals so they can quickly create their own algorithms. It is also a distributed linear algebra framework that can be used to create Machine Learning applications with high scalability. Mahout’s main focus is on classification, filtering, and collaborative grouping.
It also allows you to create your own mathematical calculations in an interactive environment. Once the code is done, you can move it into your application and then implement it.
Mahout Samsara also offers a distributed linear algebra, an engine of statistics, and a shell to link to it in production. It is often able to climb onto Apache Hadoop using the map/reduce paradigm. However, this does not limit contributions to other Hadoop implementations.
5) TensorFlow
Written by: Python Java, and Go
It is one of the most widely used Machine Learning frameworks for Java Development. It is an open-source library that uses data flow graphs to perform numerical computations. TensorFlow, without a doubt, is the bifurcated Machine Learning project. It also has the highest participation of taxpayers.
TensorFlow’s flexible architecture makes it simple for users to use a single API to execute computations on one or multiple GPUs or CPUs. This is regardless of whether the device is a desktop, server, or mobile phone.
The graph’s nodes represent mathematical operations; the edges represent multidimensional data sets (tensors), which are communicated between them.
Conclusion:-
We have now seen the top Machine Learning frameworks that are suitable for Java development. Web development using Machine Learning will revolutionize the IT industry. The popular Machine Learning libraries and frameworks are written in Python, which includes Keras and Theano, and TensorFlow. Smaller projects such as Microsoft Azure Studio, sci-kit learn, Veles, and Chainer can also be supported by Python.
It is a great time to get started if you’re interested in developing your next project using one of these Machine Learning frameworks. A reliable software development company can also help you hire skilled ML developers.
