Any given path from an input neuron to an output neuron is essentially just a composition of functions as such, we can use partial derivatives and the chain rule to define the relationship between any given weight and the cost function. Next, we need to figure out a way to change the weights so that the cost function improves. Namely, we'll develop a cost function which penalizes outputs far from the expected value. Well, for starters, let's define what a "good" output looks like. Given that we chose our weights at random, our output is probably not going to be very good with respect to our expected output for the dataset. Given our randomly initialized weights connecting each of the neurons, we can now feed in our matrix of observations and calculate the outputs of our neural network. We'll come back and revisit this random initialization step later on in the post. However, for the sake of having somewhere to start, let's just initialize each of the weights with random values as an initial guess.
#Backpropagation neuron dendrite how to#
In this post, we'll actually figure out how to get our neural network to "learn" the proper weights. In the previous post I had just assumed that we had magic prior knowledge of the proper weights for each neural network. I mentioned that we'd talk about how to find the proper weights to connect neurons together in a future post - this is that post! Overview We calculated this output, layer by layer, by combining the inputs from the previous layer with weights for each neuron-neuron connection. In my first post on neural networks, I discussed a model representation for neural networks and how we can feed in inputs and calculate an output.
0 kommentar(er)
