“In this article, I have compiled my list of top machine learning frameworks based on understanding and experience through research.”
Machine learning is one of the fastest trending technologies today. According to the report, Machine Learning and AI are going to create 2.3 million jobs by the end of 2020. The massive growth of Machine Learning projects has to lead to the evolution of numerous machine-learning frameworks.
There are various Machine Learning libraries, tools, frameworks for building Machine Learning algorithms. Choosing the perfect Machine-learning framework for your web development company is a tough task because different companies have different strategies and priorities when it comes to AI Machine Learning.
If you are unsure about which Machine Learning framework to use, look at our list of top five machine learning frameworks on the market.
Before we look at the top machine learning frameworks, let us have a look at the definition of machine learning and its importance in web development.
What is machine learning?
Machine Learning is a branch of computer science that uses statistical models to provide computers the ability to self-improve without direct human regulation. Machine Learning is seen as a subset of Artificial intelligence.
Machine Learning focuses on the development of computer programs that use training data and use it to learn for themselves to make predictions. It used in a variety of applications such as computer vision and email filtering.
The primary aim of machine learning is to enable computers to learn mechanically without human involvement and adjust actions accordingly.
Importance of Machine Learning 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 machine learning algorithms without the need to explicitly programmed. The creation of analytical models made possible with this excellent method of data analysis. These all prove the role of machine learning in web development.
Following are some of the Top Machine learning frameworks for web development
Microsoft Cognitive Toolkit:
Microsoft Research presents an open-source deep-learning toolkit for training algorithms that make it learn just like a human brain. Microsoft Cognitive Toolkit is previously known as CNTK & The Microsoft Cognitive Toolkit.
This Machine Learning framework is easily combined with other popular model types 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 choosing the parameters, algorithms, and networks. Its best feature is that it supports multi-machine-multi-GPU backends, which helps in surpassing the competitors.
Founded In: 2016
Written In: C++ and Python
TensorFlow developed by the Google Brain team.
For Java Development, this is one of the most commonly used frameworks. Also, an open-source library 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 on a mobile phone made easy with the flexible architecture of TensorFlow.
In the graphs, nodes represent mathematical operations, and edges represent multidimensional data sets (tensors) connected between them.
Founded In: November 2015
Written In: C++, Python, and CUDA
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 provides Java/Scala libraries for primitive Java collections and common math’s operations.
Mahout collaborates filtering, grouping, and classification. Also, with the help of this, one can develop their 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 that 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.
Founded In: April 2009
Written In: Java and Scala
Link: Apache Mahout
A deep learning framework mainly serves for expression, speed, and modularity. It is used for Java development and developed by the Berkey AI research team. Various personalized applications and innovations are encouraged by expressive architecture. Besides, 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 GitHub machine learning project successful.
Research institutions and industrial implements owe 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 a pre-trained model which do not require coding for implementation.
Written In: C++ and Python
The team develops it at the National University of Singapore. This deep learning platform is scalable, flexible, and is used for big data analytics. It provides scalable distributed training in large volumes of data as its architecture is flexible.
For running on a 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 that works on a group of nodes can be presented by the Apache incubator project. Deep distributed learning uses parallelization and model sharing during the process of training. Yet, Singa supports traditional machine learning models like logistic regression.
Founded In: October 2015
Written In: C++, Python, Java
Link: Apache Singha
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 the IT industry. The popular machine learning frameworks and libraries are supported by Python, which includes TensorFlow, Keras, small projects like Chainer, sci-kit learn, Microsoft Azure Studio, Veles, Neon.