Hey guys, welcome. In this video, we're going to perform convolutions with images using scipy, which is another library in Python that contains a lot of scientific computing routines. So, first of all, we're going to import the convolve and the convolve2d functions. Then we're going to load an image from scipy, they have some images, and in this case, we've preloaded this staircase image. Cool, so we're going to do a convolution of this image, with a kernel that is a three by three image. So we create this kernel, as an array, and this is what the kernel looks like. So, what do you expect this kernel to do? If we convolve the image with the kernel, we will get this result. So, as you can see, this kernel has highlighted horizontal contrast line. If a line is horizontal, it's really visible. If a line is vertical, it's almost invisible. 

Diagonal lines are still visible. So, this kernel, convolved with the image obtains highlighting of horizontal lines. Other kernels would get you different results. But essentially here, we're just showing how convolving an image with a filter, with a kernel, results in a new image, where some aspects of the image are enhanced and some other aspects are suppressed. This is the basis of how a convolutional neural network works, with the exception that, in our convolutional neural networks, the kernel will be found by the network instead of being predefined like here. Here we use an arbitrary kernel, whereas in our network, these will be weights that the network is going to find. Thank you for watching, and see you in the next video.