Deep Learning Definition
Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.
In the recent few years ago, Software development industry or IT companies might have not thought that deep learning would be going to lead the future market of products and services But now every major companies and startups have deep learning scientists who is developing intelligent products and services. Most of colleges and universities have included machine learning in academic discipline to solve real-world applications. And some applications like speech recognition and computer vision, required so much domain knowledge that they were often regarded as separate areas entirely for which machine learning was one small component.
The salient features of deep learning architectures
Deep neural networks,
Deep belief networks,
Recurrent neural networks
and convolutional neural networks
This are top four concept around which all deep learning applications and projects revolves around and some fields where this has been applied are following computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.
Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analog.
Most modern deep learning models are based on artificial neural networks. In just the past few years, deep learning has taken the world by surprise, driving rapid progress in fields as diverse as computer vision, natural language processing, automatic speech recognition, reinforcement learning, and statistical modeling.
Using this models, we can now build cars that drive themselves with more autonomy than ever before, smart reply systems that automatically draft the most tedious emails, helping people dig out from oppressively large inboxes, and software agents that dominate the world’s best humans at board games like Go, a feat once thought to be decades away. Already, these tools exert ever-wider impacts on industry and society, changing the way movies are made, diseases are diagnosed, and playing a growing role in basic sciences—from astrophysics to biology.
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