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.

 

Deep Learning: UNIT 1 : Deep Learning Fundamentals

  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

Deep Learning: UNIT 1 (B): Deep Learning: Fundamentals Part2 PPTs

                                                                             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

Deep Learning: Unit 1 (B) Notes: Deep Learning Fundamentals Part2 Notes

 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