Summary
Using a McCulloch-Pitts neuron with a logistic activation function in a one-layer network to build a reward matrix for reinforcement learning shows one way to preprocess a dataset.
Processing real-life data often requires a generalization of a logistic sigmoid function through a softmax function, and a one-hot function applied to logits to encode the data.
Machine learning functions are tools that must be understood to be able to use all or parts of them to solve a problem. With this practical approach to artificial intelligence, a whole world of projects awaits you.
This neuronal approach is the parent of the multilayer perceptron that will be introduced starting in Chapter 8, Solving the XOR Problem with a Feedforward Neural Network.
This chapter went from an experimental black box machine learning and deep learning to white box implementation. Implementation requires a full understanding of machine learning algorithms that often require fine-tuning.
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