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The era of Machine Learning is here and it’s making a lot of progress in the Technological field and according to a Gartner Report, Machine Learning and AI is going to create 2.3 million Jobs by 2020 and this massive growth has led to the evolution of various Machine Learning Frameworks. In this article, we will cover the following topics:
Machine learning is a type of Artificial Intelligence that allows software applications to learn from the data and become more accurate in predicting outcomes without human intervention.
It is a concept which allows the machine to learn from examples and experience, and that too without being explicitly programmed. To make this happen we have a lot of Machine Learning Frameworks available today. Machine Learning algorithms are an evolution of normal algorithms. They make your programs smarter by allowing them to automatically learn from the data you provide.
A Machine Learning Framework is an interface, library or tool which allows developers to build machine learning models easily, without getting into the depth of the underlying algorithms. Let’s discuss the Top 10 Machine Learning Frameworks in detail:
Google’s Tensorflow is one of the most popular frameworks today. It is an open-source software library for numerical computation using data flow graphs. TensorFlow implements data flow graphs, where batches of data or tensors can be processed by a series of algorithms described by a graph.
Theano is wonderfully folded over Keras, an abnormal state neural systems library, that runs nearly in parallel with the Theano library. Keras’ fundamental favorable position is that it is a moderate Python library for profound discovering that can keep running over Theano or TensorFlow.
It was created to make actualizing profound learning models as quick and simple as feasible for innovative work. Discharged under the tolerant MIT permit, it keeps running on Python 2.7 or 3.5 and can consistently execute on GPUs and CPUs given the basic structures.
Scikit-learn is one of the most well-known ML libraries. It is preferable for administered and unsupervised learning calculations. Precedents implement direct and calculated relapses, choice trees, bunching, k-implies, etc.
This framework involves a lot of calculations for regular AI and data mining assignments, including bunching, relapse, and order.
Caffe is another popular learning structure made with articulation, speed, and measured quality as the utmost priority. It is created by the Berkeley Vision and Learning Center (BVLC) and by network donors.
Google’s DeepDream depends on Caffe Framework. This structure is a BSD-authorized C++ library with Python Interface.
H20 is an open-source machine learning platform. It is an artificial intelligence 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 for predictive modeling, risk and fraud analysis, insurance analytics, advertising technology, healthcare, and customer intelligence.
Amazon Machine Learning provides visualization tools that help you go through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology.
It is a service that makes it easy for developers of all skill levels to use machine learning technology. It connects to data stored in Amazon S3, Redshift, or RDS, and can run binary classification, multiclass categorization, or regression on the data to build a model.
This framework provides wide support for machine learning algorithms to GPUs first. It is easy to use and efficient because of the easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms along with an extremely simple process.
To learn more about AI ML, then read the Artificial Intelligence tutorial. Also, enroll in PGP Artificial Intelligence Course by NIT Warangal to become proficient.
Cloud Machine Learning Engine is a managed service that helps developers and data scientists in building and running superior machine learning models in production.
It offers training and prediction services that can be used together or individually. It is used by enterprises to solve problems like ensuring food safety, clouds in satellite images, responding four times faster to customer emails, etc.
This Framework allows Microsoft Azure users to create and train models, then turn them into APIs that can be consumed by other services. Also, you can connect your own Azure storage to the service for larger models.
This is Apache Spark’s machine learning library. The goal of this framework is to make practical machine learning scalable and easy.
It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.
With this, we have come to the end of our Top 10 Machine Learning Frameworks list.
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