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Why is my neural network so bad?
This usually happens when your neural network weights aren’t properly balanced, especially closer to the softmax/sigmoid. So this would tell you if your initialization is bad. You can study this further by making your model predict on a few thousand examples, and then histogramming the outputs.
Why is Test loss increasing?
This is might very well be due to overfitting. In particular, note how the test loss increases but the test accuracy stays mostly the same (or even keeps increasing). This can arise from the model making wrong predictions (on the test set) with more and more certainty.
Where do neural networks fail?
They also fail whenever they are expected to predict outside of their training range. For example, train a neural net to predict the function f(x) = sin(x) when given billions of noiseless data points in the range 0 to 50. Watch it fail miserably when asked to predict f(100).
What is the function of neural networks?
Neural network, a computer program that operates in a manner inspired by the natural neural network in the brain. The objective of such artificial neural networks is to perform such cognitive functions as problem solving and machine learning.
What does neural net mean?
: a computer architecture in which a number of processors are interconnected in a manner suggestive of the connections between neurons in a human brain and which is able to learn by a process of trial and error. — called also neural net.
How are neural nets work?
Artificial neural networks are composed of layers of node
What is a neural net exactly?
A neural network is a way for a computer to process data input. They’re inspired by biological processes found in human and animal brains. Neural networks are comprised of various layers of ‘nodes’ or ‘artificial neurons’.