AI and Applications

Top 12 Artificial Intelligence tools and framework.

February 28, 2021

AI is no longer limited to Sci-Fi movies now they are deployed to real-world where every non-living person has become as smart as humans. AI played a major role in transforming the 21st century. AI has been implemented in various sectors from healthcare to education. AI has entered our daily lives to make it simpler and effortless whereas AI tools and frameworks ease the work of the developers. AI tools and frameworks made the IT sector easier and simpler for the aspirants as well as for the professionals. Since AI requires a bulky amount of data and information for processing, the tools and framework have been increased in recent years for precision outcomes. 

If you are an AI enthusiast here are the top 12 Artificial Intelligence tools and framework that you need to know


Scikit Learn is an Open Source AI tool and a python library for machine learning developed in 2007. It is built on the two most popular Python libraries NumPy and SciPy. The primary feature includes classification, regression, and clustering algorithms that enable the data scientists to quickly access the resource on anything from multiclass and multilevel algorithms. They are excellent in dealing with data analysis, data mining, and AI computations. 

Scikit learning is favorable because it expands two essential python libraries and includes calculations for data mining that includes bunching, relapse, and order.

If It is competent enough to work initially as it includes mainly main algorithms, but when it comes to complex calculations SciKit Learn is not preferable.


Tensorflow is one of the most popular AI open-source frameworks developed by Google Brain. It is ideal for handling complex calculations and bulky data handling. The programs and algorithms can be run either in GPU or CPU and are even compatible with mobile devices. They support regression algorithms, neural networks, and deep learning. It uses an agreement of multi-layered hubs that can set up, train, and send counterfeit neural systems with massive datasets at a faster rate. It is used by Google, Nvidia, AMD, etc. Tensorflow uses Python, C++, and CUDA that keeps the code clean and simple. The major drawback is it lacks pre-trained models as it is not the fastest language.


Theano is a python library suitable for complex mathematical computations. It is a powerful platform to learn deep learning. It can optimize, define, and evaluate intensive operations of multidimensional arrays. It runs python language and can execute on both CPUs and GPUs. Theano's efficiency and speed make it profitable for profound discoveries and other complex calculations. It evaluates matrix related expressions. It pairs elements of the Computer Algebra System (CAS) and that's how it gets a powerful environment to evaluate the mathematical expression. Theano is folded over Keras library and runs parallel with the Theano library. Theano is a strong competitor for TensorFlow. For AWS, Theano is a bit buggy and requires other libraries to gain a higher level of computations.


It is an intelligent framework made by keeping articulations, speed, and measured as the primary target. It is developed by the Berkeley Vision and Learning Center (BVLC). Google's DeepDream works on Caffe Framework. It is based on C++ and Python Library. They are a versatile Machine Learning framework ideal for computer vision tasks. It can switch between GPU and CPU. Caffe makes it easier to build a convolutional neural network (CNN) for image classification. The main advantage is it allows training models without codes while they are not suitable for new architecture and recurrent networks.


MxNet is an open-source AI framework that has a lot of features like writing custom layers in high-level languages. They are developed for scalability as the top priority that supports multiple GPUs. Via Memory Backdrop allows trading computation time which is essential for recurrent nets. Unlike others, it is not supported by any high corporation tech giants. In the future, it will improve the deployment support and will be versatile for the whole device.


Keras is a high-level python-based library for neural networks. It is used for image recognition, network configuration for efficient results and it can get converted into other frameworks. It uses Theano or Tensorflow as the backend. It can run seamlessly on both CPU and GPU; also new modules are easy to add. The main drawback is it cannot be used as an independent framework and not meant for an end to end the machine learning framework.


PyTorch is an open-source machine learning framework developed by Facebook. Pytorch has some cool features like TorchScriot, Distributed Training, and Python First. The source code is accessible and is available on GitHub with 22k stars. It was created to accelerate research prototyping to production deployment. PyTorch is used for computer vision and natural language processing. Pytorch has both C++ and Python interfaces. The two high level features Tensor Computing with strong acceleration via GPU and Deep Neural Networks.


The Microsoft Cognitive Toolkit is an open-source toolkit that is available for a developer for free. They are ideal for Deep Learning. The Microsoft Cognitive Toolkit allows data scientists to combine feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). It is developed to enhance commercial-grade datasets and algorithms. It is also built, trained, and runs many types of deep neural networks. It can be used as a programming language library such as Python/C++/C# Library or standalone ML tool. CNTK is flexible and allows distributed training but lacks visualization.


It is one of the most powerful machine learning tools and libraries available for developers and data scientists. AutoML automates the process by applying machine learning to the real world. AutoML is ideal for non-experts who want to put hands-on machine learning models. AutoML aims to predict unseen data where every decision in the data-driven pipeline is a hyperparameter. AutoML uses a stable ensemble of models to enhance the score. 


Open Neural Networks Library is an open-source developed in C++. It is developed for advanced ML research and Deep Learning. OpenNN delivers excellent memory management and faster processing. For data minings, it can be carried out as functions that can be installed in software through APIs. The Neural Designer tool for advanced analytics which delivers graphs and tables to analyze data entries. OpenNN is a bridge between software and predictive analytics tasks.


H20 is an open-source ML written in Java, Python, and R. Its primary application is predictive data analytics and to interpret cloud datasets in Hadoop file systems. H2O analyses data and makes decisions that enable the user to figure out insights hence, making it suitable for business. Sparkling Water is the paid version of H2O whereas there is also a standard H2O open source version. The primary aim is to detect risk and fraud analysis and predictive modeling. 

Google ML Kit

Google ML Kit is a beta SDK exclusively for mobile developers. It provides machine learning technologies with app-based APIs on devices or cloud. The features include barcode recognition, image recognition, landmark detection, and many more. It has a friendly environment to deploy machine learning into Android and iOS smartphones. Tensorflow lite models with ML Kit provide a higher level of complexity but simpler to work with.

Final Thoughts

These Artificial Intelligence tools and frameworks help to solve real-life problems. With time, the developers and data scientists will make fair use of these tools and frameworks to make a transform to all industrial sectors from healthcare, education, banking, etc. If you are using an automated machine, then probably that is the finest epitome of AL and Machine Learning.