Machine Learning

  Q1. Sparse Connection What does sparsity of connections mean as a benefit of using convolutional layers? Choose the correct answer from below: A.      Each filter is connected to every channel in the previous layer B.      Each layer in a convolutional network is connected only to two other layers C.       Each activation in the next layer depends on only a small number of activations from the previous layer D.      Regularization causes gradient descent to set many of the parameters to zero Ans: C Correct answer:  Each activation in the next layer depends on only a small number of activations from the previous layer. Reason : In neural network usage, “dense” connections connect all inputs. By contrast, a CNN is “sparse” because only the local “patch” of pixels is connected, instead using all pixels as an input. High correlation can be...

  Confusion Matrix ·        A much better way to evaluate the performance of a classifier is to look at the confusion matrix .   ·        The general idea is to ·        count the number of times instances of class A are classified as class B. ·        For example, ü   to know the number of times the classifier confused images of 5s with 3s , ü   you would look in ·        the fifth row and ·        third column of the confusion matrix . ü   To compute the confusion matrix , •         you first need to have a set of predictions so that they can be compared to the actual targets . •         You could make predictions on the test set. •         ...

  Performance Measures •         Evaluating a classifier is often significantly trickier than evaluating a regressor. •         There are many performance measures available.                            i.           Confusion Matrix                          ii.           True Positive Rate                        iii.           True Negative Rate            ...