Neural networks are based on some ideas about how neurons in the brain work, although I think there are some significant differences between computer neural networks and the neural networks in living organisms.
In machine learning, a neural network is initialized to have certain neurons connected in a particular way. Each neuron can perform a given calculation on its input and produces an output, which can go into more neurons.
You run an input through the network and get a result at the very end. Depending on whether the result is judged to be good or bad, the weights of the connections between the neurons are adjusted (this is done by a process called 'back propagation'). You run a large number of inputs, and based on the result of each run the weights are adjusted, which affects what will happen on the next run.
It's not trivial, but if you can set up the network properly, the result is that the network can effectively learn over time how to produce results that are considered good for a given input.
Modern successes in machine learning, like Deep Mind's AlphaGo, rely on the ability to use extremely large distributed computing resources to train the neural network.
I don't know much about neural networks myself, but I found this article very helpful in learning some basics: Hacker's guide to neural networks
For further actions, you may consider blocking this person and/or reporting abuse
We're a place where coders share, stay up-to-date and grow their careers.
Neural networks are based on some ideas about how neurons in the brain work, although I think there are some significant differences between computer neural networks and the neural networks in living organisms.
In machine learning, a neural network is initialized to have certain neurons connected in a particular way. Each neuron can perform a given calculation on its input and produces an output, which can go into more neurons.
You run an input through the network and get a result at the very end. Depending on whether the result is judged to be good or bad, the weights of the connections between the neurons are adjusted (this is done by a process called 'back propagation'). You run a large number of inputs, and based on the result of each run the weights are adjusted, which affects what will happen on the next run.
It's not trivial, but if you can set up the network properly, the result is that the network can effectively learn over time how to produce results that are considered good for a given input.
Modern successes in machine learning, like Deep Mind's AlphaGo, rely on the ability to use extremely large distributed computing resources to train the neural network.
I don't know much about neural networks myself, but I found this article very helpful in learning some basics: Hacker's guide to neural networks