Neural Networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.
—IBM, “Neural Networks”
Neural Networks are computing systems with interconnected nodes that work much like neurons in the human brain. Using algorithms, they can recognize hidden patterns and correlations in raw data, cluster and classify it, and – over time – continuously learn and improve.
—SAS, “Neural Networks: What they are & why they matter“
We believe that deep networks excel because they exploit a particular form of compositionality in which features in one layer are combined in many different ways to create more abstract features in the next layer.
A key question for the future of AI is how do humans learn so much from observation alone?
Communications of the ACM, June 2018, Vol. 61 No. 6, Pages 13-14
By Chris Edwards
“The secret to deep learning’s success in avoiding the traps of poor local minima may lie in a decision taken primarily to reduce computation time.”
Deep Learning Hunts for Signals Among the Noise Read More