Neural Network Prediction Example
1. They are black box - that is the knowledge of its internal working is never known
2. To fully implement a standard neural network architecture would require lots of computational resources - for example you might need like 100, 000 Processors connected in parallel to fully implement a neural network that would "somewhat" mimic the neural network of a cat's brain - or I may say its a greater computational burden
3. Remember the No Free Lunch Theorem - a method good for solving 1 problem might not be as good for solving some other problem - Neural Networks though they behave and mimic the human brain they are still limited to specific problems when applied
4. Since applying neural network for human-related problems requires Time to be taken into consideration but its been noted that doing so is hard in neural networks
5. The Vapnik-Chervonenkis dimension or VC Dimension of a neural network which is a combinatorial parameter that measures the expressive power of a neural network is still not well understood
6. They are just approximations of a desired solution and errors in them is inevitable
7. Lastly I will add that they require a large amount training set to be trained properly and to give output(s) that would be close enough to the desired output but knowing
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