Machine Learning

  UNIT-1 Deep Learning: Fundamentals Long Answer Questions ----------------------------------------------------------------------------------------------------------------------------- 1.      Explain Artificial Neural Network with example. 2.      List the different applications of Artificial Neural Network. 3.      Explain Building Block of Neural Networks with an example. 4.      Discuss Multi-Layer Perceptron (MLP) with an example. 5.      Identify the different layers in ANN. Explain them. 6.      Explain Forward Pass. 7.      Explain Backward Pass. 8.      Explain back propagation algorithm with an example. 9.      List the different optimizers. Explain them. 10.   What is the vanishing and exploding gradient problems? How to overcome those problems. Explain. 11.   List the difficulties in convergence. How to achieve convergence? Explain. 12.   Explain Preprocessing. 13.   Explain Momentum. 14.   What is Learning Rate Decay? Explain. 15.   Wh

   Deep Learning UNIT   I Deep Learning:   Fundamentals Introduction Building Block of Neural Networks Layers MLPs Forward   pass backward   pass class trainer   and   optimizer The   Vanishing   and   Exploding   Gradient   Problems Difficulties in Convergence Local and Spurious Optima Preprocessing Momentum learning rate Decay Weight Initialization Regularization Dropout SoftMax Cross Entropy loss   function Activation   Functions 👉 Deep Learning: UNIT 1 (A) Notes: Deep Learning: Fundamentals Part 1 Notes 👉 Deep Learning: UNIT 1 (A) PPTs: Deep Learning Fundamentals Part 1 PPTs 👉 Deep Learning: Unit 1 (B) Notes: Deep Learning Fundamentals Part 2 Notes 👉 Deep Learning: UNIT 1 (B): Deep Learning: Fundamentals Part2 PPTs 👉 Deep Learning: UNIT 1: Deep Learning Fundamentals -Long Answer Questions 👉 Deep Learning: UNIT 1: Deep Learning Fundamentals - Short Answer Questions

                                                                                                 UNIT-1 B Deep Learning: Fundamentals 1.      The Softmax Function 2.      Cross-Entropy Loss Function 3.      Activation Functions 4.      Preprocessing 5.      Momentum 6.      Learning Rate Decay 7.      Weight Initialization 8.      Regularization 9.      Dropout

  UNIT-1 B Deep Learning: Fundamentals 1.      The Softmax Function 2.      Cross-Entropy Loss Function 3.      Activation Functions 4.      Preprocessing 5.      Momentum 6.      Learning Rate Decay 7.      Weight Initialization 8.      Regularization 9.      Dropout