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The Back propagation algorithm in neural network computes the gradient of the loss function for a single weight by the chain rule. It efficiently computes one layer at a time, unlike a native direct computation. Calculate the output for every neuron from the input layer, to the hidden layers, to the output layer.
Hidden layers, simply put, are layers of mathematical functions each designed to produce an output specific to an intended result. Hidden layers allow for the function of a neural network to be broken down into specific transformations of the data. Each hidden layer function is specialized to produce a defined output.
When to use back propagation and forward propagation in neural networks?
If I have a 2nd hidden layer (with h2 number of units) after the 1st hidden layer and before the output layer, then what steps must I add to the forward propagation and which steps should I modify? I also have idea about how to tackle backpropagation in case of single hidden layer neural networks.
How does a multi layer neural network look like?
Firstly, let’s know how do a multi layer neural network looks like. In a multi layer neural network, there will be one input layer, one output layer and one or more hidden layers. Each and every node in the nth layer will be connected to each and every node in the (n-1)th layer (n>1).
How to build a feed forward neural network?
Build a Feed Forward neural network with 2 hidden layers. All the layers will have 3 Neurons each. 1st and 2nd hidden layer will have Relu and sigmoid respectively as activation functions. Final layer will have Softmax. Error is calculated using cross-entropy.
This is where Back Propagation comes into place. It’s nothing but updation of the weight vectors in the hidden layers according to the training error or the loss produced in the ouput layer. In this post, we are considering mutiple output units rather than a single output unit as discussed in our previous post.