Enlist different optimization algorithm which is commonly used in deep learning? Describe any one.
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Enlist different optimization algorithm which is commonly used in deep learning? Describe any one.
Describe Advantage & challenges of Deep Learning?
How Deep Learning works? Explain it?
Enlist different hyper parameter in Deep Learning? Explain any two?
Describe Vanishing gradient & exploding gradient?
Explain different application of deep learning.
Explain perceptron & multi-layer perceptron?
Define loss functions? Explain with reference to classifications.
Explain the concept of biological neuron with comparison of artificial neuron.
Illustrate the need of Activation function in Neural Networks?
Define loss functions? Explain with reference to regression.
How does a neural network get trained?
| Subject Name | Deep Learning - Elective V |
|---|---|
| Semester | VIII |
| Pattern Year | 2019 |
| Subject Code | 417532 - D |
| Max Marks | 30 |
| Total Questions | 4 |
| Duration | 1 Hour |
| Paper Number | [6411]-189 |
| Academic Year | B.E. |
| Branch Name | Artificial Intelligence and Data Science |
| Exam Type | INSEM |
| Exam Session | 2025 Mar INSEM |
| Watermark | ['CEGP013091', '49.248.216.237 13/03/2025 13:36:40 static-237'] |
What are the Advantage and challenges of deep learning.
Describe Back propagation training algorithm.
State any five applications of deep learning.
Describe under fitting, over fitting and bias variance trade-off.
Describe any five activation functions.
What are Hyper parameters? Explain any 2 in brief.
Differentiate between biological and artificial neuron.
Describe Multilayer Feed-Forward Networks.
How to select a particular activation function? Explain with example.
Explain loss functions. How to choose output and loss function? Give an example.
Explain Loss Functions for Reconstruction.
Mention Different Optimization algorithms in Deep Learning.
| Subject Name | Deep Learning - Elective V |
|---|---|
| Semester | VIII |
| Pattern Year | 2019 |
| Subject Code | 417532(D) |
| Max Marks | 30 |
| Total Questions | 4 |
| Duration | 1 Hour |
| Paper Number | [6270]-198 |
| Academic Year | B.E. |
| Branch Name | AI & DS |
| Exam Type | INSEM |
| Exam Session | 2024 Mar INSEM |
| Watermark | ['CEGP013091', '49.248.216.238 26/03/2024 13:37:47 static-238'] |
Explain the basic architecture of a Convolutional Neural Network (CNN) and describe the role of each key component within the architecture.
Define padding and strides in CNNs, and explain their impact on the output size of feature maps in convolutional layers.
Discuss the role of the ReLU layer in CNNs. How does ReLU differ from other activation functions, and why is it preferred in convolutional layers?
Describe the concept of pooling in CNNs and explain how it contributes to spatial dimensionality reduction. What are some common pooling methods, and how do they differ?
Explain the structure of Recurrent Neural Networks (RNNs) and the concept of unfolding computational graphs. How do RNNs handle sequential data, and what challenges arise with long-term dependencies?
Discuss the Bidirectional RNN and Encoder-Decoder Sequence-to-Sequence Architecture. How do these architectures enhance the ability of RNNs to handle complex sequential tasks?
Outline practical methodologies for evaluating the performance of recurrent neural networks (RNNs). Describe commonly used performance metrics, baseline models, and strategies for selecting hyperparameters in RNN-based models.
Describe the role of Long Short-Term Memory (LSTM) networks and other gated RNNs in addressing the limitations of standard RNNs. How do LSTM networks manage long-term dependencies, and what are some alternative strategies for handling multiple time scales?
What is a deep generative model, and how does it differ from discriminative models? Provide examples of deep generative models and their applications.
Describe the structure and functioning of a Boltzmann Machine (BM). What is the concept of energy in a Boltzmann Machine, and how does it guide the learning
What is a Generative Adversarial Network (GAN), and how do the generator and discriminator networks interact in the GAN framework? Describe their roles and training process.
Discuss the various types of GAN architectures and provide examples of applications where GANs have proven effective.
What is deep reinforcement learning, and how does it extend traditional reinforcement learning techniques? Discuss its significance in AI and real-world applications.
Explain the concept of a Markov Decision Process (MDP) and describe its components. How does an MDP serve as a foundation for reinforcement learning?
Describe the basic framework of reinforcement learning, including key concepts such as agents, environments, actions, states, rewards, and policies.
What are the main challenges faced in reinforcement learning, and what techniques are commonly used to address them?
Explain Q-Learning and how Deep Q-Networks (DQN) improve upon traditional Q-Learning for complex environments.
How can reinforcement learning be applied to solve a simple game like Tic-Tac-Toe? Describe the learning process and strategy.
| Subject Name | Deep Learning - Elective V |
|---|---|
| Semester | VIII |
| Pattern Year | 2019 |
| Subject Code | 417532D |
| Max Marks | 70 |
| Total Questions | 8 |
| Duration | 2½ Hours |
| Paper Number | [6404]-390 |
| Academic Year | B.E. |
| Branch Name | AI & DS |
| Exam Type | ENDSEM |
| Exam Session | 2025 May Jun ENDSEM |
| Watermark | ['CEGP013091', '49.248.216.237 29/05/2025 13:51:05 static-237'] |
What is pooling in CNNs? Why is it used? Explain the difference between max pooling and average pooling.
How do you determine the number of filters in each convolutional layer?
Define convolutional neural network (CNN), and how does it differ from other types of neural networks?
Explain the concept of padding in CNNs. Why is it used? What are strides in CNNs?
Describe the typical architecture of a CNN.
What is the main purpose of using CNNs in deep learning?
What are some common performance metrics used to evaluate RNNs? How do these metrics differ for different applications of RNNs?
Describe the long short-term memory (LSTM) unit and its components.
What is the difference between recurrent neural networks (RNNs) and feedforward neural networks?
What are hyperparameters in the context of neural networks? How do researchers select appropriate hyperparameters for training RNNs?
What is a generative adversarial network (GAN), and how does it work to generate realistic synthetic data?
Describe different types of GANs, How do these types of GANs differ in their architecture and training?
How are deep generative models used in machine learning and artificial intelligence?
What are some common challenges of using GANs, and how can they be addressed in practice?
What are some applications of GANs in computer vision?
How can reinforcement learning be applied to play Tic-Tac-Toe? What are the key components of a reinforcement learning algorithm for playing Tic - Tac.
What is deep reinforcement learning, and how does it combine deep learning with reinforcement learning?
Explain the concept of a Markov Decision Process. What are the main components of an MDP?
Describe the architecture of a deep Q recurrent network.
What are some of the main challenges faced in reinforcement learning?
| Subject Name | Deep Learning - Elective V |
|---|---|
| Semester | VIII |
| Pattern Year | 2019 |
| Subject Code | 417532D |
| Max Marks | 70 |
| Total Questions | 8 |
| Duration | 2½ Hours |
| Paper Number | [6263]-395 |
| Academic Year | B.E. |
| Branch Name | Artificial Intelligence and Data Science |
| Exam Type | ENDSEM |
| Exam Session | 2024 May Jun ENDSEM |
| Watermark | ['CEGP013091', '49.248.216.238 17/05/2024 14:05:14 static-238'] |