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

 Machine Learning - Support Vector Machines (SVM) - MCQs 1. A Support Vector Machine can be used for A.     Performing linear or nonlinear classification B.     Performing regression C.     For outlier detection D.     All of the above Ans: D 2. The decision boundaries in a Support Vector machine is fully determined (or “supported”) by the instances located on the edge of the street? Top of Form True False Ans: A 3. Support Vector Machines are not sensitive to feature scaling A.     Top of Form True False Ans: B 4. If we strictly impose that all instances be off the street and on the right side, this is called  Soft margin classification  Hard margin classification  Strict margin classification  Loose margin classification Ans: B 5. The main issues with hard margin classification are It only works if the data is linearly separable It is quite sensitive to outliers It is impossible to find a margin if the data is not l