DEEP LEARNING

International Marketing Group
IMG
  • 7th April, 2021
DEEP LEARNING

Deep Learning is about learning complex patterns in large data sets. Also known as Deep Neural Network (DNN), Deep Learning is a subfield of machine learning that enables us to model complex non-linear relationships between inputs and outputs. In the past, it was difficult to use data to make predictions because of mathematical limitations. But deep learning methods have made it easier to make accurate predictions from data sets that are much larger than before.

Deep Learning is a subset of machine learning that uses artificial neural networks. It attempts to mimic the workings of the human brain and is a branch of AI research. Deep learning has been used for decades but has recently seen a resurgence of interest due to the use of powerful new graphics processing units (GPUs) and an abundance of data.

What Are Some Examples of Deep Learning

The following are some examples of deep learning applications:

  • Image recognition: Deep learning systems can identify objects and scenes in photographs and videos. They can also generate descriptions of images, such as captions.
  • Natural-language processing: Deep learning can help computers understand human speech. These systems can carry out simple conversational actions, such as turning questions into answers.
  • Recommendation systems: Deep learning helps to make product recommendations to customers.
  • Drug discovery: Deep learning can help predict the properties of molecules, which is useful for drug discovery.
  • Robotics: Deep learning can help robots to navigate and manipulate objects.
  • Time-series: Deep learning can help to make forecasts in time-series data, such as the demand for electricity.
  • Other: Deep learning has been used for fraud detection, forecasting financial markets, and trading stocks.

What can we expect in the future from Deep Learning?

Deep learning will continue to be developed and used in a wide variety of applications. In the next few years, we will see increasingly more complex systems applied to domains that require high levels of accuracy, such as medical diagnoses and self-driving cars.

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