10 Best AI Frameworks to Create Machine Learning Applications in 2023

Artificial Intelligence is no more a science fiction term. With 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 were 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 enthusiasts 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 the 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 at the top. Coming from the Google Family, TensorFlow 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:

1. It works on any CPU or GPU as the framework is inherently capable of high computational power.
2. Uses the Python language framework which is very popular.

Capable of doing regression, and classification with the 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 TensorFlow, Microsoft Cognitive Toolkit is an open-source framework that supports text, voice, and image 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 the visual processing of images. The framework also has 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 the 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 for 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 the quick development of deep learning networks.

Popularly used in the AI labs of Facebook and Twitter, the framework adds flexibility to 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 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 backends. The Brainstorm architecture supports both CPU and GPU for neural network designing. For CPUs, the primary Python framework is used while GPUs use CUDA.

Brainstorm is not supported by a 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 the 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 is 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 the most powerful AI frameworks on the planet earth is 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.