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Artificial Intelligence: Encoder-Decoder, Auto-Encoder, PCA Quiz
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Artificial Intelligence: Encoder-Decoder, Auto-Encoder, PCA Quiz

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Questions and Answers

An Auto-Encoder consists of an NN Encoder, an NN Decoder, and a reconstruction ability.

True

PCA stands for Principle Component Application.

False

The objective in a Deep Auto-encoder is to maximize the bottleneck layer.

False

In an Encoder-Decoder for NLP, the Decoder generates the contextualized representation.

<p>False</p> Signup and view all the answers

Attention Mechanism plays a crucial role in Encoder-Decoder networks by providing flexibility but not context vector importance.

<p>False</p> Signup and view all the answers

Cross-Attention is used in neural machine translation to align and translate target sentences.

<p>False</p> Signup and view all the answers

Auto-encoder is a supervised learning method that creates a compact representation of input objects.

<p>False</p> Signup and view all the answers

The encoder and decoder in an auto-encoder neural network work independently to reconstruct the original input object.

<p>False</p> Signup and view all the answers

Deep auto-encoders always require a symmetric layer structure for more complex representations.

<p>False</p> Signup and view all the answers

Denoising auto-encoders are used to add noise to input data for better reconstruction.

<p>False</p> Signup and view all the answers

Auto-encoders can be applied in Convolutional Neural Networks (CNNs) for tasks like pooling and activation.

<p>False</p> Signup and view all the answers

Encoder and Decoder in basic RNN-based Encoder-Decoder Networks have different internal structures.

<p>False</p> Signup and view all the answers

The context vector 'c' from the Encoder carries essential input information to the Decoder.

<p>True</p> Signup and view all the answers

In Encoder-Decoder Attention models, the Attention Mechanism enables the Decoder to focus only on the final state of the Encoder.

<p>False</p> Signup and view all the answers

Attention scores determine the relevance of each Encoder hidden state to the current Decoder state.

<p>True</p> Signup and view all the answers

Transformers are a type of neural network architecture that require sequential processing of input data.

<p>False</p> Signup and view all the answers

In Transformers, the Multi-head Attention mechanism helps capture relationships between different parts of the input sequence.

<p>True</p> Signup and view all the answers

Self-Attention in Transformers is used to find relevant vectors within the output sequence.

<p>False</p> Signup and view all the answers

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