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DL Applications

Australian National University_031422A
[Australian National University]

- Overview

Deep learning (DL) is a very popular method of machine learning (ML) today and is therefore an important subfield of AI (artificial intelligence). DL uses machine learning techniques to solve real-world problems by utilizing neural networks that simulate human decision-making, so-called deep convolutional neural networks (CNNs). So DL trains machines to do what the human brain naturally does. 

The biggest feature of DL is its hierarchical structure, which is the basis of artificial neural networks, forming a deep neural network. Each layer adds to the knowledge of the previous layer. 

Deep learning tasks can be expensive, depending on massive computing resources, and AI models require large datasets to train themselves. For DL, the learning algorithm needs to understand a large number of parameters, which initially produces many false positives.

- Top DL Applications

Deep learning (DL) is the part of machine learning used to solve complex problems and build intelligent solutions. The core concepts of deep learning are derived from the structure and function of the human brain. DL uses artificial neural networks to analyze data and make predictions. It has been used in almost all commercial fields. Below are the most popular DL applications.

  • Virtual Assistants 
  • Chatbots 
  • Healthcare 
  • Entertainment 
  • News Aggregation and Fake News Detection 
  • Composing Music 
  • Image Coloring 
  • Robotics 
  • Image Captioning 
  • Advertising 
  • Self Driving Cars 
  • Natural Language Processing 
  • Visual Recognition 
  • Fraud Detection 
  • Personalisations 
  • Detecting Developmental Delay in Children 
  • Colourisation of Black and White images 
  • Adding Sounds to Silent Movies 
  • Automatic Machine Translation 
  • Automatic Handwriting Generation 
  • Automatic Game Playing 
  • Language Translations 
  • Pixel Restoration 
  • Demographic and Election Predictions 
  • Deep Dreaming


[More to come ...]


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