neural networks differ from each other by the number of neurons in each layer , number of layers , and inter-connectivity among them.
on a given neural network model , the learning process sets each neuron behavior via a small number of parameters.
while the learning process is a very demanding computation iteration, the classification itself has a low latency
in this project:
we will implement a scheduling method , designed to implement multiple neural networks classifiers , by running sub sets of the networks at time
enabling limited hardware to run big and complex networks, and fit bigger number of network classifiers concurrently.
Deep Learning
Being able to sense images and sounds in real time is just one part of human perception – there’s also all of the information surrounding the object that gives it meaning and context.
Deep Learning involves using multiple and parallel methods of training computers to recognize images or sounds, then running huge data sets to test and improve its recognition skills.
Researchers have pushed this technology forward through developer competitions and crowd-sourced projects held over the past few years.
It’s developed to the point where Deep Learning-based machine vision has surpassed human image recognition in both speed and accuracy for several types of image data.
Oddly enough, the GPU-driven “neural networks” that identify and classify image data are really good at working with natural objects: Not only can a neural network spot a dog in a photo, it can identify the breed.
what is neural network?
An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well.
neural networks differ from each other by the number of neurons in each layer , number of layers , and inter-connectivity among them.
on a given neural network model , the learning process sets each neuron behavior via a small number of parameters.
while the learning process is a very demanding computation iteration, the classification itself has a low latency
in this project:
we will implement a scheduling method , designed to implement multiple neural networks classifiers , by running sub sets of the networks at time
enabling limited hardware to run big and complex networks, and fit bigger number of network classifiers concurrently.