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HandCrafting an Artificial Neural Network
To improve the performance, we can train tens and hundreds of neural networks with different values of hyperparameters, get the output of all the networks and take their mean to get our final results.
In fact, Dropout is so easy and simple way to optimize the performance, that it gained a lot of attention recently, and is used almost everywhere in numerous other models of Deep Learning.
To implement dropout, we will use the following approach: We will first pull out random value from Bernoulli’s distribution, keep the neuron if the probability is above a particular threshold, and then perform regular forward propagation.
Researchers Made an AI Whose Performance Increases if They Let It Sleep And Dream
Of course, artificial neural networks (ANNs) - a type of artificial intelligence based on biological neural networks - don't automatically and instinctively fall asleep and dream.
'Inspired by sleeping and dreaming mechanisms in mammal brains, we propose an extension of this model displaying the standard on-line (awake) learning mechanism (that allows the storage of external information in terms of patterns) and an off-line (sleep) unlearning &
So the team worked out a way to mathematically implement human sleep patterns - rapid-eye movement sleep and slow-wave sleep, the former of which is thought to remove unnecessary memories, and the latter of which is thought to consolidate important ones.
Extensive testing using simulations validated the result - showing that allowing a neural network to nap once in a while (using the correct napping algorithm) could result in improved performance.
A.I. Shows Promise Assisting Physicians
After reshaping internet services, consumer devices and driverless cars in the early part of the decade, deep learning is moving rapidly into myriad areas of health care.
Many organizations, including Google, are developing and testing systems that analyze electronic health records in an effort to flag medical conditions such as osteoporosis, diabetes, hypertension and heart failure.
“This system can spot-check and make sure the physician didn’t miss anything.” The experimental system analyzed the electronic medical records of nearly 600,000 patients at the Guangzhou Women and Children’s Medical Center in southern China, learning to associate common medical conditions with specific patient information gathered by doctors, nurses and other technicians.
- On Thursday, May 23, 2019
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