Top 10 AI Frameworks


We are propelling into an ever-more digitally dependant universe, and the demand for immediacy and convenience grows with it. While Fritz Lang’s 1927 silent movie Metropolis portrays an eerie future run by machines, 2020 is actually far more exciting and innovative – with the science of Artificial Intelligence ubiquitous and prevalent in our daily lives.

Through its ability to deploy complex deep learning models, AI holds the capacity to transform our quality of living. As such, it comes as no shock that the future of programming hinges on this science.

So, from a programmer’s perspective, what are the best AI frameworks for creating the space for such deep learning? We’ve discovered the top ten.

1. TensorFlow

Jumping straight in with the big dog, TensorFlow is an open-source framework that combines a selection of tools, libraries, and resources that allow programmers the ability to produce holistic results in one environment. With an emphasis on simplification and streamlined development, programmers from some of the most influential brands in the world employ TensorFlow to produce AI frameworks.

2. Amazon SageMaker Neo

Amazon SageMaker Neo is a fresh service feature of Amazon’s machine learning platform. It allows programmers to train machine learning models and to operate them anywhere in the cloud. Amazon SageMaker Neo really comes into its own for real-time object detection, and with the prevalence of edge computing devices and Internet of Things (IoT) sensors (such as car cameras and surveillance systems), this AI framework technology is revolutionary.

3. Microsoft Cognitive Toolkit

This example prides itself on being multilingual, robust, and owning the strength to generate extensive and advanced machine learning datasets. This AI framework can be used as part of a project or as an independent tool by means of BrainScript, its own model description language.

4. Keras

Versatility is a strong point for this Python-written framework. Keras can be layered on top of other AI frameworks to produce even more intelligent algorithms, and it provides a space for rapid experimentation. In addition, the focus on the developer experience makes Keras a yet more attractive framework for rapid prototyping new apps.

5. Theano

For developers who prefer a Python library, Theano is another fantastic example. It entwines deep-learning abilities with NumPy to produce an evaluation of complex mathematical expressions.

6. Accord.Net

This AI framework holds value for programmers who require access to libraries with an aptitude for audio and image processing. Accord.Net is a .NET-based AI framework written in #C and focusses on comprehensive yet speedy applications to accomplish a variety of AI problems.

7. Scikit-learn

Another in the Python-written repertoire of AI frameworks, Scikit-learn helps programmers conquer widespread AI issues, as well as providing space for data mining and analysis. As an open-source platform, it offers users convenience and versatility. It holds solid foundations on NumPy, SciPy, and matplotlib, and is particularly effective for personalized recommendations and predictive analysis.

8. Apache Mahout

This framework has been created by Apache and succeeds by offering programmers the opportunity to quickly implement their own algorithms via distributed linear algebra and mathematically expressive Scala DSL. It too has the capacity to overhaul some of the major AI problems.

9. Torch

Torch holds the edge for prioritizing GPU-led machine learning algorithms. Straightforward, efficient, and wholly flexible, Torch offers programmers an ecosystem of packages to fulfill all demands of deep machine learning applications.

10. Caffe

For programmers seeking greater expression, speed, and modularity, Caffe provides an answer. The advantage of Caffe is the power to collaborate with a community of subscribers offering a communal vibe for AI machine learning tasks. It is adaptable, quick, and supports the training of models without writing code.

To Summarize…

For the growing requirement for AI technology application, it is fair to say there is an equal number of competent AI framework hosts to fulfill the most intricate of programming tasks. Depending on your language, system, and library preferences, at least one of these suggestions will embody a programmer’s requirements… Stretch Your Mind