Weights: The Heart of the Network
Explore the crucial role of link weights in neural networks, understanding their matrix representation and initialization methods. Learn to implement weight matrices in Python with NumPy, including advanced techniques for better model performance.
We'll cover the following...
We'll cover the following...
Weights
Let’s create the network of nodes and links. The most important part of the network is the link weights. They’re used to calculate the signal being fed forward, the error as it’s propagated backward, and the link weights themselves are refined in an attempt to improve the network.
The weights can be concisely expressed as a matrix. So, we can create:
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A matrix for the weights for links between the input and hidden layers: , of size .
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A matrix for the links between the hidden and output layers: ...