Machine Learning

  UNIT-1 Deep Learning: Fundamentals Short Answer Questions ----------------------------------------------------------------------------------------------------------------------------- 1.      Define Artificial Neural Network. 2.      Define Neuron. 3.      List the operations performed by ANN layers. 4.      List the different applications of Deep Learning. 5.      Define Deep Learning. 6.      List the different applications of Artificial Neural Network. 7.      List the Building Block of Neural Networks. 8.      Define Dense layer. 9.      What is loss function. 10.   Identify the different layers in ANN. 11.   Explain Forward Pass. 12.   Explain Backward Pass. 13.   List the different optimizers. 14.   How to overcome vanishing and exploding gradient problems 15.   List the difficulties in convergence. 16.   Define Preprocessing. 17.   Define Momentum. 18.   What is Learning Rate Decay? 19.   What is the purpose of weight initialization? 2

  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

Machine Learning  Introduction Concept Learning and the General to Specific Ordering Decision Tree Learning Artificial Neural Networks Bayesian Learning Instance-Based Learning Genetic Algorithms Learning Sets of Rules Analytical Learning Reinforcement Learning 👉 Machine Learning 2 Syllabus UNIT-1 :  Introduction &  Concept Learning and the General to Specific Ordering Introduction - Well-Posed Learning Problems, Designing a Learning System, Perspectives and Issues in Machine Learning, Introduction to Supervised, Unsupervised and Reinforcement Learning. Concept Learning and the General to Specific Ordering – Introduction, A Concept Learning Task, Concept Learning as Search, Find-S: Finding a Maximally Specific Hypothesis, Version Spaces and the Candidate Elimination Algorithm. 👉 Machine Learning 2- UNIT-1 (A) Notes: Introduction & Concept Learning and the General to Specific Ordering Notes 👉 Machine Learning 2 UNIT-1 (A) PPTs: Introduction PPTs 👉 Machine Learning 2- UNI