Hello Learners, In this Post, you will find **NPTEL Deep Learning Assignment 5 Week 5 Answers 2023**. All the Answers are provided below to help the students as a reference donâ€™t straight away look for the solutions.

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## NPTEL Deep Learning Assignment 5 Answers 2023:

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#### Q.1. The activation function which is not analytically differentiable for all real values of the given input is

#### Q.2. What is the main benefit of stacking multiple layers of neuron with non-linear activation functions over a single layer perceptron?

- a. Reduces complexity of the network
- b. Reduce inference time during testing
**c. Allows to create non-linear decision boundaries**- d. All of the above

#### Q.3. What will the output from node a_{3} in the following neural network setup when the inputs are (x1,x2)= (1, 1) . The activation function used in each of three nodes a_{1} a_{2} and a_{2} are zero- thresholding i.e f(x) = 1 x > 0 else 0?

- a. -1
- b. 0
- c. 1
**d. 0.5**

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#### Q.4. Suppose a neural network has 3 input 3 nodes, x, y, z. There are 2 neurons, Q and F. Q=4x+y and F=Qz2. What is the gradient of F with respect to x, y and z? Assume, (x, y, z)=(-2, 5,-4).

#### Q.5. Which of the following properties, if present in an activation function CANNOT be used in a neural network?

#### Q.6. For a binary classification setting, what if the probability of belonging to class=+1 is 0.67. what is the probability of belonging to class=-1?

- a. 0
**b. 0.33**- c. 0.67 0.33
- d. 1-(0.67 0.67)

#### Q.7. Suppose a fully-connected neural network has a single hidden layer with 10 nodes. The input is represented by a 5D feature vector and the number of classes is 3. Calculate the number of parameters of the network. Consider there are NO bias nodes in the network?

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#### Q.8. For a 2-class classification problem, what is the minimum number of nodes required for the output layer of a multi-layered neural network?

#### Q.9. Suppose the input layer of a fully-connected neural network has 4 nodes. The value of a node in the first hidden layer before applying sigmoid nonlinearity is V. Now, each of the input layerâ€™s nodes are scaled up by 8 times. What will be the value of that neuron with the updated input layer?

#### Q.10. Which of the following are potential benefits of using ReLU activation over sigmoid activation?

- a. Relu helps in creating dense (most of the neurons are active) representations
- b. Relu helps in creating sparse (most of the neurons are non-active) representations
- c. ReLu helps in mitigating vanishing gradient effect
**d. Both (b) and (c)**

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**Disclaimer**: This answer is provided by us only for discussion purpose if any answer will be getting wrong donâ€™t blame us. If any doubt or suggestions regarding any question kindly comment. The solution is provided byÂ **Brokenprogrammers**. This tutorial is only for Discussion andÂ LearningÂ purpose.

#### About NPTEL Deep Learning Course:

The availability of huge volume of Image and Video data over the internet has made the problem of data analysis and interpretation a really challenging task. Deep Learning has proved itself to be a possible solution to such Computer Vision tasks. Not only in Computer Vision, Deep Learning techniques are also widely applied in Natural Language Processing tasks. In this course we will start with traditional Machine Learning approaches, e.g. Bayesian Classification, Multilayer Perceptron etc. and then move to modern Deep Learning architectures like Convolutional Neural Networks, Autoencoders etc. On completion of the course students will acquire the knowledge of applying Deep Learning techniques to solve various real life problems.

#### Course Layout:

**Week 1**:Â Introduction to Deep Learning, Bayesian Learning, Decision Surfaces**Week 2:**Â Linear Classifiers, Linear Machines with Hinge Loss**Week 3:**Â Optimization Techniques, Gradient Descent, Batch Optimization**Week 4:**Â Introduction to Neural Network, Multilayer Perceptron, Back Propagation Learning**Week 5:Â**Â Unsupervised Learning with Deep Network, Autoencoders**Week 6:**Â Convolutional Neural Network, Building blocks of CNN, Transfer Learning**Week 7:**Â Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam**Week 8:**Â Effective training in Deep Net- early stopping, Dropout, Batch Normalization, Instance Normalization, Group Normalization**Week 9:**Â Recent Trends in Deep Learning Architectures, Residual Network, Skip Connection Network, Fully Connected CNN etc.**Week 10**: Classical Supervised Tasks with Deep Learning, Image Denoising, Semanticd Segmentation, Object Detection etc.**Week 11:**Â LSTM Networks**Week 12:**Â Generative Modeling with DL, Variational Autoencoder, Generative Adversarial Network Revisiting Gradient Descent, Momentum Optimizer, RMSProp, Adam

**CRITERIA TO GET A CERTIFICATE**:

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.

Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

**YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.**

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