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How one can enhance the “studying” and “coaching” of neural networks by means of tuning hyperparameters
In my earlier publish, we mentioned how neural networks predict and be taught from the info. There are two processes liable for this: the forward pass and backward move, often known as backpropagation. You’ll be able to be taught extra about it right here:
This publish will dive into how we are able to optimise this “studying” and “coaching” course of to extend the efficiency of our mannequin. The areas we are going to cowl are computational enhancements and hyperparameter tuning and the best way to implement it in PyTorch!
However, earlier than all that good things, let’s rapidly jog our reminiscence about neural networks!
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Neural networks are giant mathematical expressions that attempt to discover the “proper” perform that may map a set of inputs to their corresponding outputs. An instance of a neural community is depicted under:
Every hidden-layer neuron carries out the next computation:
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