Padding In order to build deep neural networks, one modification to the basic convolutional operation that we have to use is padding. Let’s see how it works. What we saw in earlier posts is that if we take a \(6 \times 6 \) image and convolve it with a \(3 \times 3 \) filter, we end up with a \(4 \times 4 \) output (or with a \(4 \times 4 \) matrix), and that’s because…
Read more

More on Edge Detection We’ve seen in the previous post how the convolution operation allows us to implement a vertical edge detector. In this post we will learn: the difference between positive and negative edges and what is the difference between light to dark versus dark to light edge transitions types of filters ( detectors ) how an algorithm can learn detector’s parameters (coefficients) A vertical edge detector Let’s have a look at this \(6…
Read more

Convolutional operation The operation of the convolution is one of the foundations of the convolutional neural networks. From the Latin word , “to convolve” means to roll together. For mathematical purposes, a convolution is the integral measuring how much two functions overlap as one passes over the other. Think of a convolution as a way of mixing two functions by multiplying them. Using the edge detection as a starting point, we will see how the convolution…
Read more

In this post we will see how to build a shallow Neural Network in Python. A Shallow Neural Network First we will import all libraries that we will use it this code.  Then we will define our datasets. Those are two linearly non-separable datasets. To getreate them we can use either make_circles or make_moons function from Sci-kit learn. We need to define activation functions that we will use in our code.  Following function initializes parameters…
Read more