Machine Learning , IOT , Microservices , python

 HERE COMES THE EASY PYTHON GAMING ENGINE DESIGN AND DEVELOP GAMES AND PLAY AND EVEN TRAIN UR GAMES FOR NEURAL NETWORKS go and download and install pretty easy - https://www.ursinaengine.org/installation.html once the installation is done run this sample from the site which is simple cube movement using keys similar to what we have played during our earlier gaming days https://www.ursinaengine.org/introduction_tutorial.html      sample  code for simple 3D game design- from ursina import * from ursina.prefabs.first_person_controller import FirstPersonController   app = Ursina()   cube_orange = Entity(model='cube', color=color.orange, scale_y=20,texture='white_cube') cube_orange_right = duplicate(cube_orange, origin_z=20) blue_cube = Entity(model='cube', color=color.blue, scale_y=2, origin_x=5,texture='vignette') duplicate(cube_orange,origin_x=10) duplicate(cube_orange,origin_x=20) duplicate(cube_orange,origin_x=30) duplicate(cube_orange,origin_x=40) dupl

sigmoid reLU

 install - python numpy import numpy as np x = [[1, 5 ,6],[7, 8 ,9],[5, 4, 3]] y = [[1,2],[2,3],[1,5]]       np.dot(x, y) result -  array([[17, 47],        [32, 83],        [16, 37]])    

 The idea is to understand how u can create a sample for stochastic gradient descent using python , numpy and some basic maths. so what is gradient descent  u might be bored with the term and it is always boring with visualization here i will run through a sample using python and plot that in mat plot lib for visual. matplot lib is another library u can install on the go and it is like a simple x,y graph that we used in our school days.don't worry this is pretty simple. 1.install python - https://www.python.org/downloads/ if you are using windows it will be a exe run that . once installed type in command line   since the function is  y = w1* x1 +w2 * x2 +b we will use the know  mean square error loss function  mae  = (y - y_hat)**2 next step would be to calculate partial derivatives for w1 , w2 , b  with respect to y dy/dw1 = x1 dy/dw2 = x2  dy/db = 1 h = (y - y_hat) ** 2  h = u **2 , u = y - y_hat dh/du = 2u = 2 (y - y_hat) du/dy = 1 by chain rule dh/dy = dh/du * du/dy dh/dy = 2

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