Deep Learning

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Transformer (machine learning model)

What is the purpose of the BERT and GPT models?

To replace RNN models such as LSTMs

To process sequential input data

To provide pretrained systems that can be fine-tuned for specific tasks

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Transformer (machine learning model)

What is the purpose of the attention mechanism in transformers?

To process tokens sequentially

To process the entire input all at once

To provide relevant information about far-away tokens

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Transformer (machine learning model)

What is the main difference between transformers and RNNs?

Transformers use attention mechanisms, while RNNs use feedforward neural networks

Transformers process tokens sequentially, while RNNs process the entire input all at once

Transformers process the entire input all at once, while RNNs process tokens sequentially

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Stable Diffusion

What is the training data used for Stable Diffusion?

Images and captions from social media platforms

Images and captions from a proprietary dataset

Images and captions from a public dataset

Images and captions generated by the model itself

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Stable Diffusion

What is the architecture of Stable Diffusion?

A convolutional neural network

A recurrent neural network

A diffusion model

A generative adversarial network

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Stable Diffusion

What is Stable Diffusion?

A natural language processing model

A deep learning, text-to-image model

A computer vision model

A speech recognition model

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Convolutional neural network

What is the difference between receptive fields in a convolutional layer and a fully connected layer?

Receptive fields are the same in both layers

Convolutional layers have larger receptive fields than fully connected layers

Fully connected layers have larger receptive fields than convolutional layers

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Convolutional neural network

What is the function of pooling layers in a convolutional neural network?

To increase the dimensions of data

To simplify the data and reduce the risk of overfitting

To add more layers to the network

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Convolutional neural network

What is the advantage of using convolutional layers in a neural network?

They require less training data

They reduce the number of free parameters

They are more accurate than other algorithms

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