Artificial Intelligence is no more a science fiction term. Each passing day this new branch of evolving science is edging closer to reality.
Siri, Google Now, Recommendation Engines and Drones are changing the way technology is becoming interactive and more humane.
Artificial Intelligence knowledge and support was once limited to a close community of genius. Time moved on very fast. AI Frameworks access is no more restricted to a limited audience. Technology enthusiast across the globe are leading the world to witness some revolutionary tools developed on Artificial intelligence as it has become more approachable.
Are you excited about your AI competence and want to transform your visualization into a tool?
First things first!
Take a call on the AI framework that supports your work in and out. How to do that? Stay glued to the article as it unfolds the potential of best AI frameworks.
Let us Dive into Ten Best Artificial Intelligence Frameworks
1 Tensor Flow
Pick up any write up that talks about AI Frameworks, Tensor Flow ranks on the top. Coming from the Google Family, Tensor Flow is trusted as a robust AI Framework that supports deep learning program developments even from a mobile device.
It gained its popularity because of two very commercial factors:
- It works on any CPU or GPU as the framework is inherently capable of high computational power.
- Uses Python language framework that is very popular.
Capable of doing regression, classification with use of computational graph abstraction, Tensor Flow is complex as one needs to have a full-scale understanding of Numpy arrays of Python Framework. The Tensor Flow framework passes the input data through multiple nodes to make a decision or prediction.
A perfect tool for statistic intensive program development. Know More here.
2 Microsoft CNTK
It is a head-on competition to Tensor Flow!
As a faster and versatile counterpart of Tensor Flow, Microsoft Cognitive Toolkit is an open-source framework that supports text, voice and images remodeling based on Deep neural networks. The framework is keeping pace with the latest development in the AI world as its architecture supports GAN, RNN, and CNN. Interlinked with Python and C++ for support, Microsoft CNTK can work with multiple servers at a time making its learning a quicker process.
Although the tool lacks a visualization board and mobile ARM support like TensorFlow. If your program needs GAN(Generative Adversarial Networks), this toolkit is perfect. Learn More.
3 Caffé
If you are in a hurry and need a pre-loaded set of pre-trained neural networks, Caffe must be your first pick. C, C++, and Python support the modeling of CNN (Convolutional Neural Networks) on Caffe Framework.
Popular for its image processing capabilities, Caffe is a deep learning network that is not capable of handling complex data but works tremendously fast for visual processing of images. The framework also has an extended support of MATLAB. Check Installation Steps.
4 Theano
Multi-dimensional numerical array computation needs a lot of computational strength and Theano is capable of delivering accuracy for such networks. Using the GPUs in place of CPU, Theano out beats the computational power of Tensor Flow Frameworks.
For all your data-intensive application development, Theano offers efficient support but would also need to be clubbed with more peripheral libraries. Supporting deep learning research, Theano has been holding its popularity despite the fact that there would be no more feature addition or power enhancement of the existing Theano Framework. Learn More About Theano
5 Amazon Machine Learning
Amazon is becoming a big technology brand. With an existing user base of its multiple services like AWS, S3 and Amazon Redshift, AML- Amazon Machine Learning offers high-end support in the development of self-learning tools.
If you do not like to work too much with the code, Amazon Machine Learning is a perfect match for you to tread in the AI space. A simple yet powerful application lets you interact with the AI-powered framework via APIs and is being extensively used by the data scientists. Visit Website.
6 Torch
Looking a framework that could support numerical operation, Lua programming language based Torch is the one you must pick. As an open source library, it offers numerous algorithms for quick development of deep learning networks.
Popularly used in the AI labs of Facebook and Twitter, the framework adds flexibility to the deep learning networks. A variant of Torch known as PyTorch is a Python-based framework, it comes across as a simpler and more reliable library for the Python enthusiasts. Know More.
7 Brainstorm
Brainstorm does not come with a beautiful UI as it works on Python scripting but is one of the most flexible and simple frameworks to work with. As its simple, it renders speed to neural network building and can work with multiple backend. The Brainstorm architecture supports both CPU and GPU for neural network designing. For CPUs, primary Python framework is used while GPUs use CUDA.
Brainstorm is not supported by well-documented framework but has an active community. Check more Info.
8 Accord.Net
If you are a C# programmer, this one is for you. Facilitating the development of neural networks that work for audio and image processing, Accord.Net is a hot favorite.
Supported by a very active development team, Accord.Net is capable enough to handle numerical intensive computation and computer visions. Pertaining to its slow speed Accord.Net did not attain a very popular and active community. Check Accord Framework.
9 Apache Mahout
An open source project library, Apache Mahout targets development of scalable machine learning frameworks. Being extensively used to support data scientists, Apache Mahout facilitates clustering, collaborative filtering, and classification. Java libraries render power to the framework for its computational operations.
It is capable of working with interfaces that need quick data to data insight transformation. Visit Website
10 Spark MLlib
Coming from the family of Apache is an extensive library and framework. Supported by R, Python, Java, and Scala, it is a framework that supports the massive scale of data processing in quick and efficient cycles.
It can be plugged into Hadoop workflows for regression, clustering, and classification as a very powerful library. Although MLibs plug and play are available for only Hadoop, it is a very popular AI framework. Check it Out.
Final thoughts on Deep Learning AI Frameworks
A comprehensive list of most powerful AI frameworks on the planet earth are in front of you. All you need to do is get started. Trusted by the use case pioneer giants of AI like Facebook and Google, these frameworks and libraries are enough to meet the requirements of any AI network developer.
Do let us know which one meets your framework needs and why. We would love to hear from you.
Hello Deepika,
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Thank you for sharing this article.