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Deep learning

Deep learning

Branch of machine learning based on learning data representations.

Deep learning is a branch of AI that enables a system to automatically recognize salient features at multiple levels of abstraction in any data set given to it. This is different from most AI systems which use mathematical modeling and computation to process a data set, but have to be trained with known data annotations.

For example, a non-deep learning AI program that wants to identify handwritten numbers typically needs a learning algorithm, a training dataset with annotations (e.g. Figure A is a '9'), and a test dataset to verify accuracy. The system uses the annotations to learn which inputs correspond to the appropriate outputs. Often the training and normal-use datasets have to be heavily preprocessed to get high accuracy, and there are many domain-specific shortcuts that can be used as well.

By contrast, a deep learning AI program simply needs a lot of data. It gradually recognizes patterns in the data which seemingly have no meaning out of context. For the handwritten numbers, the algorithm might first notice areas of light and darkness, or lines that are vertical, horizontal or curved. These observations are then passed into a higher layer which automatically combines the line observations into shapes. This process can continue with progressively higher levels of abstraction, for example into numbers, then into groups of numbers (e.g. phone numbers or other arrangements with meaning). The way this completed can be optimized in various ways, such as through gradient descent

Deep learning frameworks

A Deep Learning Framework is an interface, library or a tool which allows users to build deep learning models more easily and quickly, without getting into the details of underlying algorithms. Libraries are useful for individuals who want to implement Deep Learning techniques but don’t have robust fluency in back-propagation, linear algebra or computer math. These libraries provide pre-written code for functions and modules that can be reused for deep learning training for different purposes.

Timeline

Patents

Further Resources

Title
Author
Link
Type
Date

Deep Learning

Ian Goodfellow, Yoshua Bengio, Aaron Courville

Deep Learning and the Game of Go

Max Pumperla and Kevin Ferguson

Web

Deep Learning Crash Course

Oliver Zeigermann

Web

Deep Learning for Search

Tommaso Teofili

Web

Deep Learning with R

François Chollet with J. J. Allaire

Web

News

Title
Author
Date
Publisher
Description
Brand Post
September 29, 2021
CIO
What Defines Deep Learning? And...More Importantly, How Can Your Organization Benefit From its Implementation?

References

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