# How do filters work in CNN?

**filtering**and encoding by transformation) neural networks (

**CNN**) every network layer acts as a detection

**filter**for the presence of specific features or patterns present in the original data. The first layers in a

**CNN**detect (large) features that

**can**be recognized and interpreted relatively easy.

Simply so, what is filter number in CNN?

The **number** of filters is the **number** of neurons, since each neuron performs a different convolution on the input to the layer (more precisely, the neurons' input weights form convolution kernels).

**CNN**) is a Deep Learning

**algorithm**which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other.

Also question is, what are convolutional filters?

A **convolution** is how the input is modified by a **filter**. In **convolutional** networks, multiple **filters** are taken to slice through the image and map them one by one and learn different portions of an input image. Each time a match is found, it is mapped out onto an output image.

**Window size**, as I know it, is the length of a (sliding) cutout of a time sequence of data. E.g., if you have data x(t) that you want to model, you could use a k-**size window** x(n), x(n+1), , x(n+k). This is a method commonly used in non-recursive approximators.