Top 9 Frameworks in the AI World

Gone are the times when AI was considered to be fictional.

From robots to the Google Siri and now the introduction of the new Google Duplex, Artificial intelligence seems to have taken considerable strides to become more and more humane.

The demand for machine learning and AI has grown exponentially. Additionally, the community itself has increased as a result, and that has led to the evolution of some AI frameworks that make learning AI much easier.

In this article, we shall discuss some of the best frameworks to get you started with AI development.

Tensor Flow

Hailing from the Google family, Tensor Flow proves to be a robust open-source framework that supports deep learning and which can be accessed even from a mobile device.

Tensor flow is considered an apt tool for statistic program development. As it offers distributed training, machine models can be trained a lot more effectively at any level of abstraction that the user prefers.

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Check out this Udemy course if interested in learning Tensor Flow.

Microsoft CNTK

Microsoft CNTK is a faster and more versatile open-source framework that is based on neural networks that support text, message, and voice remodeling.

It provides an efficient scaling environment due to a faster overall evaluation of the machine models while taking care of accuracy.

Microsoft CNTK has integrations with major massive datasets, making it the leading choice to be adopted by big players like Skype, Cortana, etc., with a very expressive easy-to-use architecture as well.

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Caffe

Caffe is a deep learning network that comes along with the preloaded set of trained neural networks. This should be your first pick if your deadline is close.

Known for its image processing capabilities, this framework also has extended support of MATLAB.

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Theano

Using GPUs in place of CPU, this framework supports deep learning research and is capable of delivering accuracy for networks that need high computational power. For instance, the computation of multi-dimensional arrays requires high power, and Theano is capable of that.

Theano is based on python, which is a proven programming language when it comes to faster processing and response.

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 Amazon machine learning

Being a trending entrant to the AI community, Amazon machine learning offers high-end support in the development of self-learning tools.

This framework already has existing user base in its multiple services like AWS, S3, and Amazon Redshift. This is a managed service by Amazon, with three operations performed on the model, which are data analysis, training of the model, and evaluation.

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Torch

The Torch is an open-source framework that could support numerical operations. It offers numerous algorithms for the faster development of deep learning networks.

It is used extensively in the AI labs of Facebook and Twitter. There is a python based framework known as PyTorch, which has proven to be simpler and more reliable.

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Accord.Net

Accord.net is a C# based framework that assists in the development of neural networks used for audio and image processing.

Applications can use this commercially as well to produce computer vision applications, signal processing, as well as statistics applications.

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Apache Mahout

Apache Mahout, being an open-source framework, aims at the development of scalable machine learning frameworks. It doesn't deal with APIs as such but helps in the implementation of new machine learning algorithms by data scientists and engineers.

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Spark MLib

Spark MLib framework by Apache is supported by R, Scala, Java, and Python. It can be loaded with Hadoop workflows for providing machine learning algorithms such as classification, regression, and clustering.

Apart from Hadoop, it can also be integrated with the cloud, Apache, or even standalone systems.

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AI Framework Comparision

I hope the above helps you to choose the AI framework for your next modern application development.

If you are a developer and looking to learn deep learning to get into AI, then you may consider taking this specialized online course by Coursera.