''' swaping two numbers with temp variable '''
n1 = 1
n2 = 2
tmp = n2
n2 = n1
print('n1:',tmp,' n2:',n2)
''' swaping two numbers without temp variable '''
a = 2
b = 3
a = a+b
b = a-b
a = a-b
print('a:',a,' b:',b)
Optimization Alorithms Ex : G.D. , S.D. , Adam, RMS prop , momentum , adelta Gradient Descent is an optimization algorithm that find a best fit line and local minima of a differentiable function for given training data set. S imply used to find the coefficients (weights) and intercept (bias) that minimize a cost function as far as possible. There are three types of g radient descent techniques: Regular Batch GD (Gradient Descent) - Studiously descend the curve in one path towards one minima ; every hop calculates the cost function for entire training data. If training data is large, one should not use this. Random GD (Stochastic GD) - Calculates the Cost function for only one (randomly selected) training data per hop ; tend to jump all over the place due to randomness but due to it actually jump across minima’s. Mini Batch gradient descent - Somewhere midway between the above 2. Does the calculation for a bunch of random data poin...
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