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Tech Diaries|| 17/05/2022 || Deep Learning: Its History, Significance, and Potential Impact ||

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nadeem8
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2 years agoSteemit3 min read

Deep learning is one of the most exciting trends in artificial intelligence research today. It’s already responsible for major improvements in everything from speech recognition to natural language processing, and its potential to transform the way we interact with computers is enormous. And what can we expect from it in the future? In this post, I will answer these questions and more, exploring deep learning’s history, significance, and potential impact on our lives over the next decade and beyond.

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What Is Deep Learning

Deep learning is an exciting field of machine learning research, which has been responsible for many recent breakthroughs in computer vision, speech recognition, natural language processing and other areas. It will also introduce some basic concepts behind deep learning, including neural networks and backpropagation. It will conclude with a discussion on future directions for research in deep learning.

Types of Deep-Learning Neural Networks

There are a number of types of deep-learning neural networks. Here are some of them: Convolutional neural networks (CNNs): CNNs are used for processing visual information. AlexNet, for example, was important in achieving good performance in human image recognition benchmarks like ImageNet. RNNs (recurrent neural networks) are created to contain data across lengthy periods of time and so can be utilized for time series such as audio or textual.

What is Deep Learning's Methodology?

Deep learning systems learn to recognize patterns in data. It’s kind of like having an enormous spreadsheet that contains millions of rows of numbers (one for each piece of information), as well as a bunch of columns. All you have to do is ask deep learning how one number compares to another—is 10 bigger than 5? Does beautiful go with dog or car? And it will automatically search its massive spreadsheet for related answers.

Practical Uses of Deep Learning

Deep learning can be applied to solve problems in a range of different fields, from medical science to computer vision. For example, in medicine, a deep-learning system may be trained to detect cancer indications on an MRI image more successfully than human doctors. Deep learning is also being used to help search engines like Google better understand users’ queries.

Summary

Alternatively, if you’d like a more academic look at how different types of neural networks work check out The Neural Network Zoo on Coursera. If instead you just want a high-level explanation as well as some examples check out The Beginner’s Guide to Understanding Neural Networks by Data Camp.

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