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1. The output of a single neuron is given by y = f(w1x1 +w2x2 +w0x0), where x1 and x2 are inputs, x0 has the constant value 1, w0, w1 and w2 are parameters (weights), and f : R ! R is a function. Suggest a function f typically used in this context. Explain how this neuron could be used to classify inputs (x1; x2) into one of two classes. The input data consists of 8 points:
(1:4; 5) (2:3; 9) (9:6; 9) (0:3; 1) (a0; a1) (a2; a3) (a4; a5) (a6; a7)
where a0 a1 a2 a3 a4 a5 a6 a7 is your student enrolment number. Assign these data points to classes, in such a way that the classification could be learnt by a single neuron. Suggest values for w0, w1 and w2 that would achieve a correct classification, for your choice of function f. Draw a plot of the data points and the boundary line between the classes.
Explain in full detail how the weights of the single neuron in question 1 could be automatically adjusted using gradient descent i.e. how it could be trained to correctly distinguish the classes. Assume f to be the logistic function, and demonstrate why this choice simplifies the numerical calculation of the value of f'.
1. identify ten diagnostic medical term that use diagnostic, note the suffixes and provide the definitions.2. then identify ten related procedural medical terms that use procedural
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