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An Auto-Encoder consists of an NN Encoder, an NN Decoder, and a reconstruction ability.
An Auto-Encoder consists of an NN Encoder, an NN Decoder, and a reconstruction ability.
True
PCA stands for Principle Component Application.
PCA stands for Principle Component Application.
False
The objective in a Deep Auto-encoder is to maximize the bottleneck layer.
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.
In an Encoder-Decoder for NLP, the Decoder generates the contextualized representation.
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Attention Mechanism plays a crucial role in Encoder-Decoder networks by providing flexibility but not context vector importance.
Attention Mechanism plays a crucial role in Encoder-Decoder networks by providing flexibility but not context vector importance.
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Cross-Attention is used in neural machine translation to align and translate target sentences.
Cross-Attention is used in neural machine translation to align and translate target sentences.
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Auto-encoder is a supervised learning method that creates a compact representation of input objects.
Auto-encoder is a supervised learning method that creates a compact representation of input objects.
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The encoder and decoder in an auto-encoder neural network work independently to reconstruct the original input object.
The encoder and decoder in an auto-encoder neural network work independently to reconstruct the original input object.
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Deep auto-encoders always require a symmetric layer structure for more complex representations.
Deep auto-encoders always require a symmetric layer structure for more complex representations.
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Denoising auto-encoders are used to add noise to input data for better reconstruction.
Denoising auto-encoders are used to add noise to input data for better reconstruction.
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Auto-encoders can be applied in Convolutional Neural Networks (CNNs) for tasks like pooling and activation.
Auto-encoders can be applied in Convolutional Neural Networks (CNNs) for tasks like pooling and activation.
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Encoder and Decoder in basic RNN-based Encoder-Decoder Networks have different internal structures.
Encoder and Decoder in basic RNN-based Encoder-Decoder Networks have different internal structures.
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The context vector 'c' from the Encoder carries essential input information to the Decoder.
The context vector 'c' from the Encoder carries essential input information to the Decoder.
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In Encoder-Decoder Attention models, the Attention Mechanism enables the Decoder to focus only on the final state of the Encoder.
In Encoder-Decoder Attention models, the Attention Mechanism enables the Decoder to focus only on the final state of the Encoder.
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Attention scores determine the relevance of each Encoder hidden state to the current Decoder state.
Attention scores determine the relevance of each Encoder hidden state to the current Decoder state.
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Transformers are a type of neural network architecture that require sequential processing of input data.
Transformers are a type of neural network architecture that require sequential processing of input data.
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In Transformers, the Multi-head Attention mechanism helps capture relationships between different parts of the input sequence.
In Transformers, the Multi-head Attention mechanism helps capture relationships between different parts of the input sequence.
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Self-Attention in Transformers is used to find relevant vectors within the output sequence.
Self-Attention in Transformers is used to find relevant vectors within the output sequence.
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