Deep Learning · Module 3
Answers and explanations · Trace responsibility backward
Compare these references after your own attempt. You can read them at any time; they do not record a live model or vendor run.
Deep Learning · Module 3
Compare these references after your own attempt. You can read them at any time; they do not record a live model or vendor run.
03A. Prediction remains -0.9, error=-0.4, half-squared loss=0.08. Gradients (w,b,v,c)=(0.8,0.4,-0.44,-0.4). The paths are unchanged; only the upstream loss derivative changes.
03B. z=-0.9 and h=0, so prediction=0.2 and error=-0.3. Gradients are (0,0,0,-0.3). The inactive ReLU blocks w/b paths; h=0 blocks the gradient to v; c still directly changes the output.
03C. Simultaneous updates use all gradients from the original parameter state. At learning rate 0.01, parameters become (0.472,0.086,-0.9846,0.214), prediction=-0.800138, loss≈0.845179409522. At 0.1, parameters=(0.22,-0.04,-0.846,0.34), prediction=0.0016, loss=0.12420128. At 1, parameters=(-2.3,-1.3,0.54,1.6); ReLU closes, prediction=1.6, loss=0.605. Rate 0.1 improves this step most among these choices; it is not universally optimal.