Machine Learning

  UNIT II CNN 1.      Introduction 2.      striding and padding 3.      pooling layers 4.      structure 5.      operations and prediction of CNN with layers 6.      CNN -Case study with MNIST 7.      CNN VS Fully Connected  👉 Deep Learning: UNIT-2: CNN PPTs 👉 Deep Learning: UNIT-2 CNN Notes 👉 Deep Learning: UNIT-2 : CNN: Long Answer Questions 👉 Deep Learning: UNIT-2: CNN : Short Answer Questions

UNIT II  CNN  1. Introduction  2. striding and padding   3. pooling layers  4. structure  5. operations and prediction of CNN with layers  6. CNN -Case study with MNIST  7. CNN VS Fully Connected

                                                                                                   UNIT II  CNN  1. Introduction  2. striding and padding   3. pooling layers  4. structure  5. operations and prediction of CNN with layers  6. CNN -Case study with MNIST  7.  CNN VS Fully Connected

  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 found between the sparseness of the output of different layers, which makes CNN better than t