Best Machine Learning Frameworks to try in 2021.
We are in the era of Machine Learning and it’s making a lot of progress in the Technological field. According to a Gartner Report, Machine Learning and AI is created 2.3 million Jobs by the end of 2020 and this massive growth has leded the evolution of various Machine Learning Frameworks.
The adoption of Machine learning is seeing increased across many industries. Since 2022, there will be a surge in the amount of machine learning scientists and the two aspects of the technology, mathematical and implementational.
Machine learning frameworks are libraries in which that helps developers to create ML models and applications without using any core algorithms or technicalities. Each framework are designed to meet a different purpose and also can solve a variety of business problems.
There are number of machine learning frameworks. Given that each one take time to learn, and given that some have a wider user base than others, which one should you use?
In this article, we have taken a high-level look at the major ML frameworks and some newer ones to the scene:
1. Tensorflow
Google’s open-source platform TensorFlow is perhaps one of the most popular tool for Machine Learning and Deep Learning. TensorFlow software library that has a comprehensive and flexible ecosystem of tools, libraries, and community resources. It is a JavaScript-based and it comes equipped with a wide range of tools and community resources that facilitate easy training and deploying ML/DL models.
The core tool of TenserFlow allows you to build and deploy models on browsers, you can also use TensorFlow Lite to deploy models on mobile or embedded devices. Tensorflow leverages data flow graphs in which the branches (tensors) of data can be processed by a series of algorithms on a graph. The movement of this type of data on the system is known as flow, hence the name Tensorflow. This helps to build and train ML models using intuitive APIs like Keras which can be also be used in speech recognition systems, image and video recognition and tagging, self-driving cars, text summarization, and sentiment analysis softwares.
2. H2O
H2O is an open-source machine learning framework that provides access to Machine Learning algorithms like Python, Java, Scala, R, big data systems, and data sources. H2O also be used as a solution to collect data, build models, and serve prediction. It is an AI tool which is business-oriented and helps in making a decision based on data and enables the user to draw insights. It is mostly used in predictive modeling, risk and fraud analysis, insurance analytics, Digital advertising , healthcare, and customer intelligence.
3. Theano
Theano is folded over Keras. It’s an abnormal state neural systems library, that runs nearly in parallel with the Theano library. Keras’ fundamental system are favorable position is that it is a moderate Python library for profound discovering that can keep running over Theano or TensorFlow.
Theano was created to make actualizing profound learning models as quick and simple as feasible for innovative work. Discharged under the tolerant MIT permit, it keep running on Python 2.7 or 3.5 and it can consistently execute on GPUs and CPUs given the basic structures.
4. Amazon Machine Learning
Amazon machine learning is a cloud-based service system that are suitable for all developers to deploy machine learning. It boasts visualization tools and a wizard’s tools that help you go through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology. Amazon Machine Learning makes it easy to extract predictions for applications using simple APIs. This system can analyze and predict customer behavior, recognize message content, predict quantities and intervals of customer service inquiries, personalize web services for customers and classify documents.
Amazon Machine Learning provides service that makes it easy for developers of all skill levels to use machine learning technology. It connects the data stored in Amazon S3, Redshift, or RDS, and can also run binary classification, multiclass categorization, or regression on the data to build a model.
5. Sci-Kit Learn
Scikit-learn is one of the most well-known Machine Learning libraries. It is an open library for data analysis written in Python for general purposes. Scikit-learn is based on other Python libraries like NumPy, SciPy, and Matplotlib. It contains several implementations for different Machine Learning algorithms. Scikit-learn can handle both supervised and unsupervised learning with a wide variety of algorithms and can utilities the perfect tool to start programming and structuring data analysis and statistical modeling systems. Scikit-learn framework involves a lot of calculations for regular AI and data mining assignments, including bunching, relapse, and order.
Summary
A Machine Learning Framework is an interface, library or tool in which that allows developers to build machine learning models easily, without getting into the depth of the underlying algorithms. We’re sure that trends will change dynamically, so what’s top of mind this year may lose popularity the next end.
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