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Last video I laid out the structure of a neural network. Iamp;#39;ll give a quick recap here so that itamp;#39;s fresh in our minds, and then I have two main goals for this video. The first is to introduce the idea of gradient descent, which underlies not only how neural networks learn, but how a lot of other machine learning works as well. Then after that weamp;#39;ll dig in a little more into how this particular network performs, and what those hidden layers of neurons end up looking for. As a reminder, our goal here is the classic example of handwritten digit recognition, the hello world of neural networks. These digits are rendered on a 28x28 pixel grid, each pixel with some grayscale value between 0 and 1. Those are what determine the activations of 784 neurons in the input layer of the network. And then the activation for each neuron in the following layers is based on a weighted sum of all the activations in the previous layer, plus some special number called a bias. Then you
