Assembling a Neural Network from Perceptrons
Learn how to design a neural network by assembling perceptrons.
We'll cover the following
Recap
The first
Even for a simple dataset like MNIST, our perceptron was scraping by, making almost one mistake every ten characters. With neural networks, we can aim for an order of magnitude better accuracy. In this part of the chapter, we’ll build an MNIST classifier that reaches 99% accuracy, which is one error every 100 characters.
Now let’s design a neural network that classifies MNIST digits.
Assemble perceptrons
Let’s see how to build a neural network, starting with the perception that we already have. As a reminder, the perceptron is a weighted sum of the inputs, followed by a sigmoid:
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