Significance of Deep learning
Deep learning is a set of groundbreaking new inventions, tools, algorithms, and extraordinarily diverse ways to do certain tasks. Compared to any machine, it is about machines being cognizant and taking on the roles traditionally reserved exclusively for humans because of their far superior intelligence capabilities. The fundamental idea of deep learning and Artificial neural networks is derived from the structure of a biological nervous system.
In the last few decades, deep learning has made some significant strides in various fields and domain. In this post we’d acquaint ourselves with some of the most useful applications and implementations of deep learning.
1) Self Driving Cars: Companies developing such driver assistance programs, as well as full-blown self-driving vehicles such as Google's, need to train a machine how to take over key bits (or all) of driving instead of the senses of a person using artificial sensor systems. To do that, businesses normally start with training algorithms that use a large volume of data.
2) Image Recognition: Image recognition is another common field of deep learning. It attempts to recognise and classify contents in photographs like people and objects as well as to explain the content and context. In many industries, such as gaming, social media, retail, tourism, etc., image recognition is still being used.
3) Advertising: Another primary area which has been changed by deep learning is advertisement. Both publishers and marketers have used it to increase the relevance of their commercials and raise their promotional strategies' return on investment.
4) Automatic Colorization: The task of applying color to pictures is image colorization. Deep learning can be used to color a picture using the objects and their background within the photograph, just as the problem might be approached by a human operator. This power optimizes ImageNet-trained and co-opted high-quality and very large fully convolutional neural networks for image colorization.
5) Handwriting Generation: This is a process where, given a corpus of handwriting samples, new handwriting for a given word or phrase are generated.
The handwriting is given after the handwriting samples have been produced as a series of coordinates used by a pen. The relationship between the pen movement and the letters is learned from this corpus, and fresh examples can be produced ad hoc.
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Ashpreet Kaur - Jul 2, 2021
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