Machine learning frameworks are interfaces that permit developers to make and execute machine learning models more rapidly without any problem.
Across the globe, businesses are highly adopting operational digitization with the help of machine learning and artificial intelligence. In today’s market, many machine learning frameworks are developed, so the selection of the correct machine learning framework for your business can be tricky.
Best Machine Learning Frameworks
Machine Learning is a concept that allows the machine to learn without being programmed. The machine absorbs all the changes from previous experience and work.
This article will help you track down the top 10 Machine Learning Frameworks in this year.
1. TensorFlow
Tensorflow is a highly recommended ML framework that works on both CPUs and GPUs. It is an open-source project so that the developers can use APIs for development and structure ML framework free of cost. TensorFlow is a python library that executes and constructs dataflow graphs in C++.
You can use TensorFlow in two ways either by installing via NPM (Node Package Manager) or via script tags. TensorFlow has the disadvantage of not being learner-friendly for the new learners. TensorFlow can execute classification, neural networks.
2. PyTorch
Primarily developed by Facebook AI Research PyTorch is also an open-source ML library centered around Torch library. Caffe2 and one more ML framework were merged into PyTorch by the FAIR team in early 2018. PyTorch is known for its speed. It has faster training- time that can make a difference on big projects.
PyTorch has a primary competition with TensorFlow. Just like TensorFlow, PyTorch is an open-source ML framework that runs on both CPUs and GPUs. PyTorch does regression, classification, and neural networks just like TensorFlow. PyTorch is more customizable compared to TensorFlow.
3. Sci-kit Learn
Sci-kit learn is a user-friendly open-source platform for beginners as it comes with thorough documentation. It is rated the finest for data mining and analysis by the users. Sci-kit is a frequently used library for ML in Python. It is mostly used for administered and unsupervised calculations.
Sci-kit is also a python platform that can be beneficial for linear regression, SVMs, Stochastic Gradient Descent Models, K-nearest regression, Decision Tree regression, and many more. It allows users to change algorithms while running the program.
4. Caffe
Caffe is an open-source deep learning framework which is written in C++ with Python interference. It is a BSD-authorised. It is developed by the University of California, Berkely.
Google’s deep dreams work on the Caffe framework. Caffe is frequently used in academic research projects, start-up projects, and big industrial submissions.
5. H2O
H2O is an open-source ML framework developed to organize the decision-support of the process of the system. H2O supports the most frequently used algorithms such as gradient boosted machines and many more. It is mostly used for analytics of the insurance customers, for analysing the data of the healthcare patients, customer intelligence, and many more.
H2O has three versions, currently, H2O-3 is being used in the ML framework. H2O-3 is compatible to work with java, Python, Scala, and JSON. H2O is expandable so that users can easily transfer working files to multiple computers. H2O is adaptable for the changes as per client systems and can customize the algorithm when needed.
6. CNTK
CNTK, sometimes known as Microsoft cognitive toolkit, is developed by Microsoft research. It is an open-source ML framework designed to describe the progress phases by directed graphs. CNTK 2.7 is the latest version.
CNTK allows users to easily understand the concepts of large scale neural networks. It helps developers to merge different ML frameworks. Python, C++ can use CNTK as a library. You can also use CNTK from the JAVA manuscript.
7. Shogun
Shogun is also an open-source ML framework that works well in C++.
Developers use shogun to design algorithms for research purposes. Shogun mostly connects with the other libraries. And is compatible with other languages. Shogun is learner-friendly and helps implement hidden Markov models.
8. Apple’s core ML
Core ML is a framework that can be used to integrate machine learning. Core ML is mainly used by students and new learners. It is highly comprehensive and offers new features that include image classification, game kit, barcode scanner, object processing, and many more.
It has improved security according to the developed features and ensures the privacy of the user’s data.
9. Torch
Torch is an open source ML library and script language based on Luna programming language. It is an old ML library released in 2002. It provides a flexible N-dimensional array to support basic indexing. Torch mainly works with GPUs.
It is very efficient due to the ease of users. With a torch, you can have maximum flexibility and speed up constructing the scientific algorithms with extreme ease. As it is built with Luna, data structures are easy to build. Torch supports functional programming so it is very easy for users to create complex coding without having to start over every time there is a mistake.
10. Apache MxNet
Apache MxNet is used by Amazon in AWS as a learning tool. It is accessible on various servers as well as GPUs. MxNet supports almost 8 languages including Python, Java, and C++. Research facilities such as the University of Hong Kong and the University of Washington (in which it was co-developed) supports MxNet.
MxNet is built to work on cloud structures by using parameter servers. It is easier for developers who are used to imperious programming. It is easier for debugging, tracking, and modifying learning rates.
Wrap up
Easy availability and user-friendliness makes machine learning Frameworks so useful in our day-to-day life. Machine learning is not a new technology but the abrupt representation has been fruitful to learners for development. As we are developing new features with artificial intelligence, machine learning provides the suitable platform we need.
This list here highlights wildly used machine languages. With the dependence of the algorithm you need and with the production, budget you can choose your preferable machine learning framework.
