**Important RGPV Question **

Table of Contents

Toggle**AL 503 (B) Deep Learning **

**V Sem, AIML**

**UNIT 1-Introduction History of Deep Learning**

**Q.1) **Explain the historical progression of Deep Learning, highlighting key milestones and breakthroughs that have shaped its development.

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**Q.2) **Assess the representation power of Multilayer Perceptrons (MLPs) and discuss their capacity to learn complex relationships in data.

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**Q.3)** Discuss the Backpropagation algorithm and its role in optimizing the weights of neural networks during the training process.

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**Q.4) **Discuss the GPU implementation of randomized SVD and write its applications.

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**Q.5)** What is the representation power of a multilayer network of sigmoid neurons? Explain in detail.

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**Q.6) **What is batch normalization? Explain how does it work and also write its advantages.

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**UNIT 2-Deep Feedforward Neural Networks**

**Q.1) **Investigate the theoretical foundations and practical implications of AdaGrad, Adam, and RMSProp optimization algorithms in the context of training deep neural networks.

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**Q.2) **Explain the architecture and working principles of Deep Feed forward Neural Networks. Discuss their application areas and key advantages over other types of neural networks.

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**Q.3)** ) Discuss the role of regularization in auto-encoders and its impact on the training process.

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**Q.4) **Discuss the challenges associated with encoding and decoding sequential data and how RNNs handle these challenges through their inherent architecture.

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**Q.5)** Explain in brief sparse auto-encoder and contractive auto- encoder.

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**Q.6) **Write a short note on regularization of auto encoders.

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**Q.7) **When should auto encoders be used instead of PCA/SVD for dimensionality reduction? Justify.

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**UNIT 3- Introduction to Convolutional neural Networks**

**Q.1) **Explain the concept of Convolutional Neural Networks (CNNs) and their role in image recognition tasks.

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**Q.2) **Define the ReLU activation function and explain its significance in CNNs.

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**Q.3)** Explain the concept of Deep Dream and its role in generating surreal images based on neural network activations.

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**Q.4) **Discuss in detail about ReLu activation function.

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**Q.5)** Illustrate the CNN terms Padding and Pooling.

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**Q.6) **What is unit pruning? Discuss the need of unit pruning in deep learning.

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**UNIT 4- Introduction to Deep Recurrent Neural Networks**

**Q.1) **Explain the fundamental concepts and various architectures of Deep Recurrent Neural Networks (RNNs).

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**Q.2) **Explain the challenges of vanishing and exploding gradients during the BPTT process and their impact on the network’s ability to learn long-range dependencies.

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**Q.3)** Explain briefly about directed graphical model.

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**Q.4) **Explain briefly deep recurrent neural networks and its architecture with a neat diagram.

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**Q.5)** Discuss in detail Long Short Term Memory (LSTM) and write the advantages of LSTM.

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**UNIT 5-Introduction to Deep Generative Models**

**Q.1) **Explain the concept of value iteration in dynamic programming for solving Markov Decision Processes (MDPs).

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**Q.2) **Explain briefly about the following auto regressive models.

a) NADE

b) MADE

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**EXTRA QUESTIONS-**

**Q.1) **Compare and contrast policy iteration with value iteration in terms of convergence and computational complexity.

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**Q.2) **Compare and contrast different least squares methods, such as LSPI (Least Squares Policy Iteration).

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**Q.3)** Explore advanced Q-learning algorithms, such as Double DQN and Dueling DQN.

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**Q.4) **Write a short note on any two of the following:

a) Representation learning.

b) Feed forward neural network

c) Weight decay and also the benefits of using weight decay

d) Deep belief networks

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**— Best of Luck for Exam —**