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UNIT 3

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• Basics

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Questions

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Recurrent Neural Networks

1. What is a Recurrent Neural Network (RNN)? Explain its working mechanism.

• Answer

1. Differentiate between Feed-Forward Neural Networks and Recurrent Neural Networks.

• Answer

Types of Recurrent Neural Networks

1. Explain the types of Recurrent Neural Networks (RNNs) with suitable examples.

• Answer

Feed-Forward Neural Networks vs Recurrent Neural Networks

1. Compare Feed-Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs) in detail.

• Answer

Long Short-Term Memory Networks (LSTM)

1. Explain Long Short-Term Memory Networks (LSTMs) in detail with a suitable diagram.

• Answer

Encoder-Decoder Architectures

1. Explain encoder-decoder architectures and their applications.

• Answer

Recursive Neural Networks (RvNNs)

1. What are Recursive Neural Networks (RvNNs)? Discuss their working mechanism.

• Answer

1. Discuss the advantages and disadvantages of Recursive Neural Networks in natural language processing (NLP).

• Answer

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UNIT 4

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• Basics

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Questions

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1. Undercomplete Autoencoders

• Q4 b) State and explain Undercomplete Autoencoders.
• Answer

2. Regularized Autoencoders-Sparse Autoencoders

• Q4 a) Explain Regularized Autoencoders-Sparse Autoencoders. [9]
  • Answer

3. Denoising Autoencoders

• Q3 b) Describe Denoising Autoencoders, Contractive Autoencoders. [9]
• Answer
• Q4 b) Explain Denoising Autoencoders with a suitable figure.
• Answer

4. Contractive Autoencoders

• Q3 b) Explain the concept of Contractive Autoencoder and its need. [8]
• Answer

5. Applications of Autoencoders

• Q3 a) Discuss different types of Applications of Autoencoders. [9]
• Answer
• Q4 a) State the applications of Autoencoders. Explain how the dimensionality reduction feature of autoencoder is useful in information retrieval tasks. [9]
• Answer

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UNIT 5

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• Basics

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Questions

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1. Greedy Layerwise Pre-training

• Q5 a) Discuss Greedy Layerwise Pre-training methods. [9]
• Answer
• Q5 a) Why is the network called Greedy Layerwise Pretraining Network? [9]
• Answer

2. Transfer Learning and Domain Adaptation

• Q6 b) Explain Transfer Learning and Domain Adaptation. [9]
• Answer
• Q6 b) Justify when to use domain adaptation and when to use transfer learning. [9]
• Answer

3. Distributed Representation

• Q6 a) Explain distributed representation with example. [9]
• Answer

4. Variants of CNN: DenseNet

• Q5 b) Explain DenseNet in detail. [9]
• Answer
• Q6 a) Elaborate Variants of CNN in detail. [9]
• Answer

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• Basics

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graph TD
    A["ALL THE BEST! 🌟"]:::special --> B["@HustlerX01"]:::username
    B --> C["You've Got This! 💫"]:::motivation
    C --> D["Stay Focused 🎯"]:::action
    C --> E["Stay Calm 🧘"]:::action
    C --> F["Stay Confident 💪"]:::action
    D --> G["Success Awaits! 🏆"]:::success
    E --> G
    F --> G

classDef special fill:#E3F2FD,stroke:#1976D2,stroke-width:3px,color:#0D47A1
classDef username fill:#E8F5E9,stroke:#388E3C,stroke-width:2px,color:#1B5E20
classDef motivation fill:#FFF3E0,stroke:#F57C00,stroke-width:2px,color:#E65100
classDef action fill:#F3E5F5,stroke:#7B1FA2,stroke-width:2px,color:#4A148C
classDef success fill:#FAFAFA,stroke:#424242,stroke-width:3px,color:#212121

%% Using a softer, more professional color palette
%% Improved contrast for better readability
%% Consistent color theme across nodes
%% Balanced stroke widths for visual harmony

Connect with me on Telegram: @HustlerX01 🚀