Showing posts with label ANN. Show all posts
Showing posts with label ANN. Show all posts

Deep Learning: UNIT-1 : Deep Learning Fundamentals- Short Answer Questions

 UNIT-1

Deep Learning: Fundamentals

Short Answer Questions

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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?

20.  What is Regularization?

21.  List different Regularization techniques.

22.  Define Dropout.

23.  Define SoftMax activation function.

24.  When we use cross entropy loss function?

25.  List the different activation functions.

26.  Define sigmoid activation function.

27.  Define tanh activation function.

28.  Define ReLU activation function.

29.  How to train the neural network?


30.  Compare the ReLU activation function with the sigmoid activation function.

Deep Learning UNIT-1 Deep Learning: Fundamentals: Long Answer Questions

 UNIT-1

Deep Learning: Fundamentals

Long Answer Questions

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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.  What is the purpose of weight initialization? Explain.

16.  What is the purpose of Regularization? Explain different techniques of Regularization.

17.  Explain Dropout with example.

18.  Explain softmax activation function with example.

19.  When we use cross entropy loss function? Explain.

20.  List the different activation functions. Explain them.

21.  How to train the neural network? Explain.

 

About Machine Learning 2

Machine 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- UNIT-1 (B) PPTs: Concept Learning and the General to Specific Ordering PPTs

👉Machine Learning 2 - UNIT-1 Questions

UNIT-2: Decision Tree Learning & Artificial Neural Networks

Decision Tree Learning – Introduction, Decision Tree Representation, Appropriate Problems for Decision Tree Learning, The Basic Decision Tree Learning Algorithm, Issues In Decision Tree Learning.

Artificial Neural Networks- Introduction, Neural Network Representation, Appropriate Problems for Neural Network Learning, Perceptrons, Multilayer Networks and the Back-Propagation Algorithm.

👉Machine Learning 2 - UNIT-2 (A) NOTEs: Decision Tree Learning

👉Machine Learning 2 - UNIT-2 (A) PPTs : Decision Tree Learning PPTs

👉Machine Learning2 - UNIT-2(B) Notes: Artificial Neural Networks Notes

👉Machine Learning2 - UNIT 2(B) PPTs : Artificial Neural Networks PPTs

👉Machine Learning2 - UNIT 2 : Decision Tree and Artificial Neural Network Questions

UNIT-3: Bayesian Learning & Instance-Based Learning

Bayesian Learning – Introduction, Bayes Theorem, Bayes Theorem and Concept Learning, Bayes Optimal Classifier, Naive Bayes Classifier, Bayesian Belief Networks, EM Algorithm.

Instance-Based Learning- Introduction, K-Nearest Neighbor Algorithm, Locally Weighted Regression, Remarks on Lazy and Eager Learning.

👉Machine Learning 2 - UNIT-3 (A) Notes: Bayesian Learning Notes

👉Machine Learning 2 : UNIT-3 (A) PPTs: Bayesian Learning PPTs

👉Machine Learning 2 : UNIT-3 (B) NOTEs: Instance-Based Learning NOTEs

👉Machine Learning 2 : UNIT-3 (B) PPTs: Instance-Based Learning PPTs

👉Machine Learning2- UNIT-3 Questions

UNIT-4: Genetic Algorithms & Learning Sets of Rules

Genetic Algorithms – Motivation, Genetic Algorithms, An Illustrative Example, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic Algorithms.

Learning Sets of Rules – Introduction, Sequential Covering Algorithms, Learning Rule Sets: Summary, Learning First-Order Rules, Learning Sets Of First-Order Rules: FOIL

👉Machine Learning2 - UNIT-4 (A) NOTEs: Genetic Algorithms NOTEs

👉Machine Learning2- UNIT -4 (A) PPTs: Genetic Algorithms PPTs

 👉Machine Learning2: UNIT-4 (B) NOTEs: Learning Sets of Rules NOTEs

👉Machine Learning2: UNIT-4 (B) PPTs: Learning Sets of Rules PPTs

👉Machine Learning2: UNIT-4 Questions

UNIT-5: Analytical Learning & Reinforcement Learning

Analytical Learning- Introduction, Learning With Perfect Domain Theories: PROLOG-EBG, Explanation-Based Learning Of Search Control Knowledge.

Reinforcement Learning – Introduction, The learning task, Q–learning, Nondeterministic, Rewards and Actions, Temporal Difference Learning, Generalizing from Examples, Relationship to Dynamic Programming.

👉Machine Learning2: UNIT-5(A) NOTES: Analytical Learning NOTES

👉Machine Learning2: UNIT-5(A) PPTs: ANALYTICAL LEARNING PPTs

👉Machine Learning2: UNIT-5(B) NOTEs: Reinforcement Learning NOTEs

👉Machine Learning2: UNIT-5(B) PPTs: Reinforcement Learning PPTs

👉Machine Learning2: UNIT-5 Questions


Text Books:

1. Machine Learning – Tom M. Mitchell, – MGH

2. Machine Learning: An Algorithmic Perspective, Stephen Marsland, Taylor & Francis (CRC)

Reference Books:

1. Machine Learning Methods in the Environmental Sciences, Neural Networks, William W Hsieh, Cambridge Univ. Press.

2. Richard o. Duda, Peter E. Hart and David G. Stork, pattern classification, John Wiley & Sons Inc., 2001.





 



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