Geoffrey Hinton, professor at the University of Toronto and engineering fellow at Google Brain, recently published a paper on the Forward-Forward algorithm (FF), a technique for training neural networ ...

The phrase deep learning refers to that network depth, the hierarchical structure of the neural network on which today’s whole deep-learning revolution has been built.” In recent years, the ML field ...

This week, you will have two short quizzes, a Jupyter lab programming assignment, and an accompanying Peer Review assignment. This material, notably the backpropagation algorithm, is so foundational ...

Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep learning ...

This reversing process is known as backpropagation and is a main feature of machine learning in general. An enormous amount of variety is encompassed within the basic structure of a neural network.

Training algorithm breaks barriers to deep physical neural networks EPFL researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the ...

Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) for postoperative mortality of 9.17 (95% CI, 5.85-13.82) compared with an unadjusted OR of 2.08 (0. ...

On the 10th anniversary of key research that led to deep learning breakthroughs, ... whose 1986 paper popularized the backpropagation algorithm for training multilayer neural networks.

Hinton’s backpropagation algorithm allowed LeCun to train models deep enough to perform well on real-world tasks like handwriting recognition.

It was first published in 1986 in a paper titled "Learning representations by back-propagating errors." From Wikipedia, the definition of backpropagation is: In machine learning, backpropagation is a ...