In deeplearning, neural network most of the time uses various layers for doing a work. The neural networks can be both simple and complex. The concept of working with several layers comes from the functions of the brain.
In this image, we are seeing a neural network with 3 layers. The input x is taken as 0th layer. Then the layer containing three circles is the 1st layer. The circles are usually called units and they are actually information. After that, the 2nd layer that comes is the output layer where we get the output after passing an input x to a machine learning model. Lastly we use logistic regression to find out if the data is effective or not. Now, we are also going to see an example of complex neural network layers. Below is the image of a more complex neural network layer.
In this image, we can see that there are more layers than the previous image. In this image, the outputs of a particular layer are working as the input of the next layer. In this case a formula is also working. For determing the result of a particular unit of a given layer, a formula is used which is the equation of a straight line.
Here, g = wa + b is the formula. In this formula w and a are vector quantities. This is all about neural network layers.
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