AI : Popular Tools & Technologies

Artificial Intelligence

Popular Tools & Technologies

The objective of artificial intelligence is to develop machines that learn to perform tasks like humans and eventually do them better than humans. The tools used towards that end are designed to understand actions of humansand machines and replicate these, predict the next logical step, and improvise and improve the output. The approach developers commonly take to achieve this combine statistical methods and computational intelligence, while the tools they use include logic and methods based on probability,etc.

 

Among the popular AI tools are Tensorflow, PyTorch, Scikit-Learn, MicrosoftCNTK, Seaborn, and some others. Some of the popular ones are open-source and hence commonly used by developers. Let us find out what each of these offer.

 

TensorFlow

This is an open-source software library. The system, though initially developed by Google for is researchers and engineers working in its Machine Intelligence division, is general enough to be applied in a host of other domains. TensorFlowis used forperforming high-end numericalcomputations, deep neuralresearch, image recognition, voice and facial expression and natural language processing.

MicrosoftCNTK

Microsoft CNTK, also known as Microsoft Cognitive Toolkit, is a deep learning framework developed by Microsoft Research. It is an open-sourceand easytousetoolkitthattrainsdeeplearningalgorithmstolearnlikehuman brains. The toolkit allows distributedtraining, supports C, C++, Java andPython, the preferred languages of many developers.

 

Scikit-Learn

This too is an open-source Python library used for machine learning offering a range of algorithms such as Clustering, Regression and Classification. Scikit-Learn is prefered for machine learning and AI because it offers dimensionalityreduction, bundle of classificationalgorithms, unsupervised learning algorithms and clusteringalgorithms and efficient for datamining.

Keras

Keras is written in Python and is yet another open-source neural-

network library. It is capable of running on top of Tensorflow, Microsoft Cognitive Toolkit, R, Theano, etc. Keras Python also deals with Neural Networks. It offers consistent and simpleAPIs, minimises the number of user actions required for common use cases, and provides clear and actionable feedback upon usererror.

OpenCV

This is a cross-platform library which focuses mainly on image processing, video capture and analysis including features like face detection and object detection.

 

PyTorch

PyTorch has a production-ready Python library with excellent applications, demos and use cases. It includes a machine learning compiler called Glow that boosts the performance of deeplearningframeworks.

NumPy

This is another Python library that deals with complex mathematical operations like linear algebra, Fourier transformation, random number and features that work with matrices and n-dimensional arrays inPython.

Pandas

The Pandas library is built on top of Numpy, which means it needs the latter to operate. Pandas helps manage complex data operation with just one or two commands i.e. it serves as the best starting point to create a more focused and powerful data tools.

Matplotlib

Matplotlib is a visualization library written in Python for 2D plots of arrays. This too is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.

 

Seaborn

Seaborn is unique and an exceptional visualization library. Based on Matplotlib foundations, it offers advanced-level dataset based interface to make high-quality statisticalgraphics.Seaborn’s features allow developers to perform statistical estimation when combining data across observations, plotting and visualizing the suitability of statistical models to strengthen datapatterns.