Machine Learning , IOT , Microservices , python

import numpy as np import matplotlib.pyplot as plt import cv2 as cv #this program conversts color of certain sections of image #each pixel is checked for the r g b and modified using a numpy array image = cv.imread('rg.png') #print(image.shape) #print(np.asarray(image)) width , height , channels = image.shape #print(width) #print(height) #print(channels) plt.imshow(image) plt.show() #print(image[29][29]) zero_array = np.zeros((2, 3, 3)) for pixeli in range(width):     #print(pixeli)     for pixelj in range(height):        # print(pixeli,pixelj)         #print(image[pixeli][pixelj])         pixelvalue = image[pixeli][pixelj]         #print(pixelvalue)         if pixelvalue[0] == 76 and pixelvalue[1] == 177 and pixelvalue[2] == 34:             #print('mach')             r = 0             g = 0             b = 0             image[pixeli][pixelj] = [r, g, b]   plt.imshow(image) plt.show() The pixel level color are changed as y

Simply called as Conv net , neural networks dealing with images simply called convnet. use case : Models can be created using Conv net and trained using data.     Simple convnet can be created using tensorflow    

Raspberry pi is a computer which can easily run on linux os uses: can be used as a controller for projects  controller meaning send and receive data using raspberry pi to devices  for testing. can be used for developing prototypes. simply buy and start exploring the world simple as that. https://www.raspberrypi.org/products/   if your are interested in LINUX install in pi and use as a desktop computer.   https://ubuntu.com/download/raspberry-pi  https://ubuntu.com/download/iot Buy the latest raspberry -5 Select your country and reseller to buy directly from the reseller. https://www.raspberrypi.com/products/raspberry-pi-5/ Raspberry keyboard can be bought but my experience with the keyboard was boring and some keys were not working  It’s better to use other keyboards rather than this.   buy the new raspberry with higher configuration  8 Gb/quad core  enjoy - https://www.raspberrypi.com/products/raspberry-pi-400/