ANNs consist of a large number of simple interconnected processing elements. These elements operate in parallel, whose function is determined by network structure, connection strengths, and the processing performed at computing elements or nodes. The growth of interest in deep learning was partly due to the failures of traditional programming techniques in "hard" tasks such as machine vision, continuous speech recognition, and machine learning. There have been significant demonstrations of neural network capabilities in vision, speech, signal processing, and robotics. The variety of problems addressed by neural networks is impressive.

From Brain Research to Artificial Neural Networks Construction of First Neural Networks Layered Construction of Neural Network From Biological Brain to First Artificial Neural Network Current Brain Research Methods Using Neural Networks to Study the Human Mind Simplification of Neural Networks: Comparison with Biological Networks Main Advantages of Neural Networks Neural Networks as Replacements for Traditional Computers Working with Neural Networks References Neural Net Structure Building Neural Nets Constructing Artificial Neurons Attempts to Model Biological Neurons How Artificial Neural Networks Work Impact of Neural Network Structure on Capabilities Choosing Neural Network Structures Wisely "Feeding" Neural Networks: Input Layers Nature of Data: The Home of the Cow Interpreting Answers Generated by Networks: Output Layers Preferred Result: Number or Decision?

By using feature inversion to visualize millions of activations from an image classification network, we create an explorable activation atlas of features the network has learned which can reveal how the network typically represents some concepts.