Top Machine Learning Frameworks for Web Development
Following points supports the importance of Machine learning web development
- Serves as a best replacement for conventional data mining methods
- Protects from security threats
- Generates customized information and content
- Stocks of machine learning APIs
- Helps in understanding customer behavior
- Helps in quicker product discovery
With the help of machine learning, computers can learn the algorithms without the need of explicitly programmed. Creation of analytical models are made possible with this finest method of data analysis. These all proves the role of machine learning in web development.
Following are some of the Top Machine learning frameworks for the web development
Microsoft Cognitive Toolkit:
It is an open source deep learning toolkit which is presented by Microsoft for training algorithms which makes it to learn just like human brain. Various machine learning models can be used by this framework like feed-forwards DNNs, convolutional neural networks, and recurrent neural networks. The main motto behind designing this tool is to use the neural networks for understanding large unstructured datasets. It is highly customizable owing to the faster training times, easy to use architecture, which further allows to choose the parameters, algorithms, and networks. Its best feature is that it supports multi-machine-multi-GPU backends, which helps in surpassing the competitors.
For Java Development, this is one of the most commonly used framework. It is also an open source library that uses data flow graphs for numerical computation. It is a bifurcated machine learning project on GitHub and it has more participation of taxpayers. The computations on one or more GPU or CPU with a single API either on desktop or mobile phone is made easy with the flexible architecture of TensorFlow.
In the graphs, nodes represent mathematical operations, edges represent multidimensional data sets (tensors) connected between them.
It is an open source offering Apache designed for data scientists, statisticians, and mathematicians for executing various algorithms quickly. It is also a distributed linear algebra framework with the help of which machine learning applications have scalable performance. Mahout collaborates filtering, grouping, and classification.
Also, with the help of this, one can develop their own mathematical calculations in an interactive environment which runs on a big data platform, the same code can be moved into the app and can be implemented. Mahout samsara provides an engine of statistics and a distributed algebra which works and distributes together with an interactive shell and the library links the app production. Using maps/ reduce paradigm, it climbs onto the Apache Hadoop platform but will not restrict its contributions in implementing others based on Hadoop.
It is a deep learning framework which mainly serves for expression, speed and modularity. It is used for Java development and is developed by Berkey AI research team. Various personalized applications and innovations are encouraged by expressive architecture. Also, switching between GPU and CPU is possible with configuration options like configuring a single indicator. The extensible code of Caffe resulted in having early growth and making GitHube machine learning project successful.
Research institutions and industrial implements owes to the speed of Caffe. Its development is mainly due to the computer/classification vision using convolutional neural networks. With the help of Model Zoo, a set of pre-trained model which do not require coding for implementation.
It is developed by the team at National University of Singapore. This deep learning platform is scalable, flexible and is used for big data analytics. This provide scalable distributed training in large volumes of data as its architecture is flexible.
For running on wide range of hardware, it is extensible. Its main apps are in image recognition and natural language processing (NLP). At present, a simple programming model which works on group of nodes can be presented by Apache incubator project. Deep distributed learning uses parallelization and model sharing during the process of training. Yet, Singa supports the traditional machine learning models like logistic regression.
Above mentioned are some of the top machine learning frameworks used for Java development. This is evident that Machine learning is going to be the future of IT industry. The famous machine learning frameworks and libraries are supported by Python which includes TensorFlow, Keras, small projects like sci-kit learn, Microsoft Azure Stuido, Chainer, Veles, Neon.