Archives for feature extraction

23 Jun

How Feature Extraction Can Be Improved With Denoising

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image-5276

The life cycle of a machine learning models involves a training phase, where a typical data scientist develops a model with good prediction based on historical data and features extracted from the data at hand. This model is then put into production with the hope that it would continue to have similar predictive performance during…

The post How Feature Extraction Can Be Improved With Denoising appeared first on Analytics India Magazine.

28 Nov

Deep Learning Has Become The Go-To Method For Problem Solving

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image-1110

Deep learning has been one of the most popular substrates of the machine learning family, in the last decade or so. The reason for its popularity is its sky-high accuracy. But what makes deep learning so integral in problem-solving? The ‘deep’ part of deep learning comes with more than one layer in its architecture, along…

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25 Sep

Why Convolutional Neural Networks Are The Go-To Models In Deep Learning

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image-291

Over the years, research on convolutional neural networks (CNNs) has progressed rapidly, however the real-world deployment of these models is often limited by computing resources and memory constraints. What has also led to extensive research in ConvNets is the accuracy on difficult classification tasks that require understanding abstract concepts in images. Another reason why CNN […]

The post Why Convolutional Neural Networks Are The Go-To Models In Deep Learning appeared first on Analytics India Magazine.

17 Sep

How Machine Learning Is Revolutionising The Study Of Galaxies With Image Classification

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image-190

There are billions of galaxies in space. Morphological galaxy classification is based on their shapes and general visualisation, and it is important for astronomers because it gives them an account of their composition and evolution. The most popular morphological classification scheme known as the Tuning Fork, proposed by scientist Edwin Hubble. In this classification (refer […]

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