How do neural networks interpret words, and human language?
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The basic idea behind a neural network is to simulate lots of densely interconnected brain cells inside a computer so you can get it to learn things, recognize patterns, and make decisions in a human like way.
Information flows through a neural network in two ways. When it's learning or operating normally patterns of information are fed into the network via the input units, which trigger the layers of hidden units, and these in turn arrive at the output units.
This common design is called a feed forward network. Each unit receives inputs from the units to its left, and the inputs are multiplied by the weights of the connections they travel along.
Every unit adds up all the inputs it receives in this way and if the sum is more than a certain threshold value, the unit "fires" and triggers the units it's connected to.
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How do neural networks interpret words, and human language?
=> Two of the possible approaches to the problem: Treat it as a standard classification problem. You have a regular feed-forward neural network, where you train it on your.