Deep Learning: Fundamentals of Deep Learning for Beginners (Artificial Intelligence Book 3) by Rudolph Russell

Author:Rudolph Russell [Russell, Rudolph]
Language: eng
Format: epub
Published: 2018-06-30T23:00:00+00:00

As you can see, there are instances in which the model captures some noise,

evidenced by any deviations from the shape of the logistic function. But all the lines produced are overall a good generalization of the logistic function that underlies the pattern of the data. This is an easy display of the MLP model’s ability to handle non-linear functions. This concept also holds true in other practical examples.

Limitations and Considerations for MLP Models

It is a problem if you are using a back-propagation algorithm, when the error is a function of the weights; that convergence upon a global optimal can be hard to solve.

For example, when we are trying to optimize non-linear functions, many

local minima obscure the global minimum. We can therefore be tricked into thinking we’ve found a model which can effectively solve the problem when in fact we’ve chosen a solution that doesn’t effectively reach the global minimum.

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