Deep Learning is the new big trend in Machine Learning. It promises general, powerful, and fast machine learning, moving us one step closer to AI. Over the past few years, the term “deep learning” has firmly worked its way into business language when the conversation is about Artificial Intelligence (AI), Big Data and analytics.
Use of Deep Learning
One use of deep learning networks is named-entity recognition, which is a way to extract from unstructured, unlabeled data certain types of information like people, places or companies. That information can then be stored in a structured schema to build, say, a list of addresses or serve as a benchmark for an identity validation engine.
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With data transforms, a deep network is capable of understanding audio signals. This can be used to identify snippets of sound in larger audio files and transcribe the spoken word into text. The best example of Speech-to-Text is google voice search.
Image Recognition( Image Classification )
An image recognition algorithm takes an image or a patch of an image as input and outputs what the image contains in it. The output is a class label ( e.g. “Wolf”, “Tiger”, “Car” etc. ). The Question is, how does image recognition algorithm know the contents of an image? Well, you will have to train the algorithm to learn the differences between different classes.
The best example of Image Recognition can be the Google’s Image Search.
Deeping Neural Networks
Deep Neural Networks are basically Neural Networks with more than one Hidden layers. According to NNADL, Neural networks approach the problem in a different way. The idea is to take a large number of handwritten digits.
The neural network uses these examples to automatically infer rules for recognising handwritten digits. By increasing the number of training examples, the network can learn more about handwriting, and so improve its accuracy.
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