Neural networks are computational systems modeled after the neurons found in human brains that can process inputs and generate outputs, similar to how neurons do. They're used for numerous applications including computer vision and machine learning.
Neural Networks Learn Fast and Adaptively
These sophisticated computer models have the ability to rapidly acquire knowledge on solving specific problems, then apply it when new issues arise. Neural networks can be utilized in numerous tasks from air traffic control to recognizing objects in photographs.
How Neural Networks Work
A neural network typically comprises several processors operating simultaneously and organized in layers. Each tier receives raw input information - similar to what neurons of our human brain receive - from previous tiers before processing and providing answers for any given query.
Each processor maintains a small database containing inputs it has received over time as well as any rules it was initially programmed with or created itself. Each tier's processors connect directly with other tiers and the output layer.
Neural networks feature interconnected layers that permit signals from each tier to pass easily between them, eventually reaching the output layer. A typical neural network begins with receiving input data at one tier before passing it along to another layer which, in turn, sends it back through to its source at the first layer, etc.
Once a node meets its threshold value, it becomes activated, sending its output signal onward to the next tier of processors for further processing. Once complete, all answers from all tiers are passed onto an output layer that determines whether their result is "true" or false.
Error and Gradient Derivatives
For any network to train successfully, it must understand how its error changes with its weights. This can be accomplished by computing its error and then adjusting its weights in response. An algorithm called gradient descent provides the means by which its parameters can be updated: it finds both direction and rate for updating.
Backpropagation refers to an approach used for updating weights or bias. When updating these variables, one would calculate their respective partial derivative of error function and use that value to modify weights or bias accordingly.
This process yields a weight or bias to be added to an existing network, improving its accuracy. Furthermore, updating all weights or biases simultaneously is also possible.
Nonlinear Operations in Neural Networks
Each node in a neural network features its own activation function that determines its output. These activation functions may take the form of sigmoid or tanh functions and typically follow logistic regression principles.
Neural networks are an artificial intelligence (AI). They possess the capacity to learn "how" to solve a problem from its inputs, then apply that knowledge when faced with new ones. Neural networks have numerous uses including air traffic control, object recognition in photographs and many more tasks.
