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Neural networks are networks that rely on backward regression analysis on inputs. Initially you place X inputs into the network and state which outputs you would like it to have. Error reduction configures the network's internal weights to allow the X inputs to grant Y outputs.
Essentially following a grand heuristic:
1. INPUTS * WEIGHTS = CalculatedValue
2. Does CalculatedValue equal the TrueValue?
3. If yes, don't change weights
4. Change weights to reduce error
I recently created a program to analyze my handwriting, honestly believe the future will implement neural networks everywhere. It's horrifyingly beautiful how it mimics nature to solve questions that traditionally could only be solved by humans. For instance, using neural networks we can recognize dogs, cats, trees, etc. I strongly believe that the future is the consolidation of relevant data into some giant archive of knowledge. Humans can use our phones, glasses, cars, etc to analyze the environment using the archive. We can then use the archive to understand the world. A world where we can use a phone to check what plant that is, what that object is made of, what star is that, or even, scarily, who is that girl over there.
Essentially following a grand heuristic:
1. INPUTS * WEIGHTS = CalculatedValue
2. Does CalculatedValue equal the TrueValue?
3. If yes, don't change weights
4. Change weights to reduce error
I recently created a program to analyze my handwriting, honestly believe the future will implement neural networks everywhere. It's horrifyingly beautiful how it mimics nature to solve questions that traditionally could only be solved by humans. For instance, using neural networks we can recognize dogs, cats, trees, etc. I strongly believe that the future is the consolidation of relevant data into some giant archive of knowledge. Humans can use our phones, glasses, cars, etc to analyze the environment using the archive. We can then use the archive to understand the world. A world where we can use a phone to check what plant that is, what that object is made of, what star is that, or even, scarily, who is that girl over there.
