Jul 3, 2020
Jul 3, 2020
Machine Learning (ML) is one of the fastest-growing technologies today. It is the subset of AI, which helps the business examine the data, learn, and adapt it. Based on its data analysis, machine learning enables the devices to make a decision and work accordingly.
With the help of ML frameworks, you can easily construct ML models, which are accurate and efficient. Build a successful mobile application using ML frameworks. and so as a developer, you might be getting confused about using the right framework. Here you can get information about the best machine learning frameworks.
Machine learning give computers the ability to learn without explicitly programmed. Giving you greater control over their business with a self-service. AI platform that runs continuously to eliminate blind spots – examples include Image processing, medical diagnosis, prediction, classification, learning association, regression, etc.
Some familiar machine learning frameworks include, PyTorch, TensorFlow, Scikit-learn, Apache MXNet, FireBase ML Kit, Keras, Microsoft Azure, and Sonnet. The development tools that meet your requirements including popular IDE’s, Jupyter notebooks, and CLI’s or programming languages such as Python, PERL, RoR must be used.
PyTorch is another popular framework for machine learning. Facebook developed it. This framework is based on the Torch library. It is designed to advance the entire process from research prototyping to production deployment. It has a C++ frontend atop and a Python interface. PyTorch makes use of standard debuggers like PDB or PyCharm.
Keras is an open-source machine learning framework. This framework is more faster than the other frameworks. It comes with in-built support for data parallelism and handle a large numbers of data. This framework is written in Python and it is very easy-to-use. You can use this for high-level computation.
Scikit Learn supports development work in Python with an extensive library for Python programming language. Users are rate this framework for on of the best data mining and data analysis. It provides support for classifications, clustering, pre-processing, regression, Dimensional reduction, and Model selection.
Apache MXNet was adopted by Amazon as a Machine Learning tool for AWS. It distributed on a cloud infrastructure via a parameter server. It is scalable across several GPUs and servers.
Microsoft Azure Machine Learning is a cloud-based predictive-analytics service. It comes with a browser-based tool that provides a very easy, drag, and drop interface called Azure Machine. Learning Studio (ML Studio) for building machine learning models. The generated model can easily deploy as a Web Service that can be consumed by any programming language of your choice.
If you are looking for a high-end framework for machine learning, then you must consider learning Sonnet. Sonnet is using for building complex neural network structures in TensorFlow. It's a simple but powerful programming model. It is based on a single concept.
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