@@ENF15 y=mx+c wher m=y2-y1/x2-x1=slope=weight if m=0 then y=0x+c =>y=c so c is not equal to slope =m any query feel free to ask y and x are variables x is input /independent and y is output/dependent variable c is a value which at place in y-axis In neural networks, each connection between neurons has a weight, and each neuron has a bias.
Neural Networks = 1:00
Discriminative Models = 29:00
Generative AI = 39:00
Transformer Models = 50:00
Junaid Bhai explain Transformer = 1:45:00
Q/A = 2:00:00
thanks to all teachers. clearly understand about transformers logic.
@27:53 the bais in first layer is 5 and in 2nd layer it is also 5. so total parameter=75
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Thanks sir
Sir y constant nai random hota ha means ye change hota ha x ki Waja se.
no c is constant
@@ahmedbadar3398 of course C is constant which is equal to slope cofficient but y is random means change as the value of x changes.
@@ENF15 y=mx+c wher m=y2-y1/x2-x1=slope=weight if m=0 then y=0x+c =>y=c so c is not equal to slope =m any query feel free to ask y and x are variables x is input /independent and y is output/dependent variable c is a value which at place in y-axis In neural networks, each connection between neurons has a weight, and each neuron has a bias.
@@ahmedbadar3398 Dear brother please understand my messages and if confusion you may consult any statistics book.
29-aug-2024=q=3class=7