jgs Neural Network § And Where is the “learning” part? § It is in the calculation of the W values Calculate W values sig (W1*X1 + W2*X2 + W3*X3 + W4*X4)
jgs Forward propagation § Use “random” values for W § Provide the network with one input and observing the output. § It is just linear algebra. § Calculate the error
jgs Backward propagation § Your output will probably have a high error. Minimize it. How? § A proper adjustment of weights ensures that the subsequent output will be closer to the expected output. Repeat until the network can produce results significantly close enough to the known output. § Partial derivative of the cost function with respect to any weight in the network. § We want to see how the function changes as we let just one of those variables change while holding all the others constant. § Black-box
jgs Problem § Your output will probably have a high error. Minimize it. How? § A proper adjustment of weights ensures that the subsequent output will be closer to the expected output. Repeat until the network can produce results significantly close enough to the known output. § How to modify the value of the weights? Short answer: use the gradient descent algorithm (1847), i.e., Partial derivatives of the error function with respect to each weight § We want to see how the function changes as we let just one of those variables change while holding all the others constant.
jgs Back propagation Did I mention something called learning rate? Review Details (Math) Here: https://medium.com/analytics-vidhya/neural-networks-in-a-nutshell-with-java-b4a635a2c4af
Ph.D. [email protected] Spring 2022 Copyright. These slides can only be used as study material for the class CSE205 at Arizona State University. They cannot be distributed or used for another purpose.
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