FAIL AND LEARN

An image as simple as 200x200 is too much information for a Nueral network or a computer to understand. The machine learning algorithm like logic regression cannot process the 200x200 information of image, even if it does, the results may not be favourable at all. The accuracy of Nueral Network totally depend upon the input we've given it. So in machine learning the NN's preprocess the input and extract necessary information from the input. Something advance such as CONVOLUTION NUERAL NETWORK(CNN) perform feature extraction. In order to extract feature from an image we need convolution in image processing. Convolution is process in signal processing which is used to combine 2 signals. More about convolution in here . Here the convolution in computer vision is for feature extraction . We extract important details from image like Edges,noise removal,etc... The extraction of important data can be achieved by using a KERNAL .  The kernal method is used for sharping,embrose,smoo

In this post we are gonna learn about fourier transformation and implementation of Discrete fourier transformation in python. Why we need to use fourier transformation anyway...a normal periodic wave like sine wave has 3 characterstics like Phase,amplitude,frequency that tell us about the wave..it's easy and understandable but what if a wave is unperiodic like this ex. in this example it is not a periodic sinewave..so but an multiple frequencies of waves added together. Hence to analyse these kind of waves we need fourier transformation in signal processing. The fourier transforamation is applied in both Continous and Discrete waves . .In this we are gonna concentrate in DFT(Discrete fourier transformation)..and to visulalize the wave we are gonna use python(cause it's easy to use). we need the following python library to do this : 1.numpy    2.seaborn    3.mathplotlib     DFT(DISCRETE FOURIER TRANSFORMATION) It is applied to specifically discete signals. It is used to break th