The rise of Artificial Intelligence has helped simplify the world around us. No longer a subject matter of science fiction, AI is the truth behind the digital revolution of the 21st century. While AI applications have found their place across various industrial sectors, it has also given rise to the number of AI tools and frameworks. Just as AI applications help simplify our lives, AI tools and frameworks help simplify the task of a developer.
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For every budding aspirant seeking to make it big in the AI domain, the question that inevitably comes to mind is –
“What are the best frameworks and tools for Artificial Intelligence?”
To end this dilemma, we have compiled a list of the best tools and frameworks for Artificial Intelligence. Here goes!
- Scikit-learn
Scikit-learn is an open-source, Python-based ML library that was developed in 2007. It is built on two of Python’s most widely-used libraries – NumPy and SciPy. It includes a host of administered and unsupervised learning calculations for standard AI and data mining functions (for example, bunching, relapse, order, choice trees, k-implies, etc.). Scikit-learn is excellent for dealing with AI computations, data analysis, and data mining.
- TensorFlow
TensorFlow is an open-source software library developed by Google Brain. It is the perfect tool for handling high-volume, complex numerical computations. TensorFlow features a multi-layered arrangement of hubs that allow developers to promptly set up, train, and send counterfeit neural systems with massive datasets. It is used by tech giants like Intel, Google, SAP, and Nvidia.
- Keras
Keras is a Python-based library framework that can run on top of other frameworks like TensorFlow or Theano. It is a handy tool for image recognition problems, network configuration for optimal results, and choosing the best-suited architecture for specific issues. Keras has a unique abstract feature that allows it to convert into other frameworks smoothly.
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- Theano
Theano is a Python library best suited for working with complex Mathematical expressions. It allows you to define, optimize, and evaluate math expressions comprising of multi-dimensional arrays. Theano is folded over the Keras library that runs almost parallelly and seamlessly with the Theano library.
- OpenNN
OpenNN (Open Neural Networks Library) open-source library written in the C++. It is designed for Deep Learning and advanced ML research. OpenNN delivers optimal memory management and high processing speed. It comes with extensive documentation and unit testing features. As for data mining techniques, OpenNN implements them as a bundle of functions that can be embedded in other software tools through APIs. This enables the interaction between software tools and predictive analytics tasks.
- H2O
H2O is an open-source ML software tool. It was designed by H2O.ai and is written in R, Java, and Python programming languages. It is mostly used by AI developers and researchers for predictive data analytics and to analyze cloud datasets in Apache Hadoop file systems. H2O can be a nifty tool for businesses as it can enable data-driven decision making.
- PyTorch
PyTorch is an open-source ML framework designed to expedite the journey from research prototyping to production deployment. Created by Facebook, PyTorch comes with brilliant features like TorchScript, Python-First, and Distributed Training.
- Caffe
Caffe is a versatile ML framework that was created by the Berkeley Vision and Learning Center (BVLC) and community donors. Caffe’s expressive architecture encourages speed and innovation. It is an excellent choice for computer-vision tasks
- Google ML Kit
As the name suggests, Google ML Kit (beta) is Google’s ML SDK designed for mobile app development. It allows developers to build highly customized features on both Android and iOS platforms. This kit is equipped with video and image analysis APIs, NLP APIs, and the cutting-edge AutoML Vision Edge feature.
- Microsoft Cognitive Toolkit (CNTK)
The Microsoft Cognitive Toolkit is an open-source toolkit for Deep Learning. It allows Developers to combine model types like feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). CNTK can either be used as a standalone ML tool through its unique model description language (BrainScript), or as a library in Python/C++/C# programs.
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At present, what we have seen AI do is merely the tip of the iceberg. With time, as the AI technology advances, so will these tools and frameworks. As ML and AI experts find better ways to utilize these tools, we may be looking forward to a radical transformation across all industries including healthcare, banking & finance, retail, IT, and other domains.
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