DL_Yes/NO_Quiz7

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True or false

Creada por

Sivaranjini S

India

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Educaplay Educational Resources

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Responde si estas canciones ganaron el Grammy a Mejor canción o grabación del año (los generales, no cuentan los Grammys de categorías)
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Imágenes, canciones... Ya nada es lo que parece. ¿Están hechas con IA o son reales?
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Un juego de preguntas y respuestas para niños sobre el fútbol. Deberán responder con ✅ o ❌ a una serie de afirmaciones relacionadas con este deporte.
OPEN UP 5 UNIT 3: THERE IS/ARE, THERE WAS, THERE WERE

karen blixen

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Estudiamos el grammar de la Unidad
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Responde si las siguientes frases sobre cultura general son verdaderas o no.


DL_Yes/NO_Quiz7Versión en línea True or false por Sivaranjini S 1 Non-Maximum Suppression (NMS) is applied to the RPN's proposals before they are passed to the detection head, in order to reduce redundancy among highly overlapping proposals. Yes No 2 DenseNet differs from ResNet in that DenseNet concatenates feature maps from all preceding layers within a dense block, whereas ResNet adds the input of a block to its output. Yes No 3 In BPTT, gradients are computed only for the final time step. Yes No 4 Transposed convolution is used to reduce spatial dimensions in segmentation networks Yes No 5 In GANs, the generator and discriminator are trained simultaneously in a competitive manner. Yes No 6 FPN relies on multiple resized input images like traditional image pyramids. Yes No 7 Skip connections increase the number of learnable parameters significantly. Yes No 8 Two-stage detectors first classify objects and then generate region proposals. Yes No 9 A predictive model trained on time-stamped patient data without temporal validation can overfit by leaking future information into the training set. Yes No 10 The GAN generator is trained without ever directly seeing real data — it learns only via gradient signals back-propagated through the discriminator. Yes No

DL_Yes/NO_Quiz7Versión en línea

True or false

por Sivaranjini S

Non-Maximum Suppression (NMS) is applied to the RPN's proposals before they are passed to the detection head, in order to reduce redundancy among highly overlapping proposals.

Yes

DenseNet differs from ResNet in that DenseNet concatenates feature maps from all preceding layers within a dense block, whereas ResNet adds the input of a block to its output.

Yes

In BPTT, gradients are computed only for the final time step.

Yes

Transposed convolution is used to reduce spatial dimensions in segmentation networks

Yes

In GANs, the generator and discriminator are trained simultaneously in a competitive manner.

Yes

FPN relies on multiple resized input images like traditional image pyramids.

Yes

Skip connections increase the number of learnable parameters significantly.

Yes

Two-stage detectors first classify objects and then generate region proposals.

Yes

A predictive model trained on time-stamped patient data without temporal validation can overfit by leaking future information into the training set.

Yes

The GAN generator is trained without ever directly seeing real data — it learns only via gradient signals back-propagated through the discriminator.

Yes

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DL_Yes/NO_Quiz7

Sí o No

Sobre esta actividad

Creada por

Descarga la versión para jugar en papel

Top juegos

Sí o No

Grammys a mejor canción/grabación

Sí o No

¿Está creado con Inteligencia Artificial?

Sí o No

Preguntas sobre futbol para niños

Sí o No

OPEN UP 5 UNIT 3: THERE IS/ARE, THERE WAS, THERE WERE

Sí o No

¿Es verdad?

DL_Yes/NO_Quiz7Versión en línea

por Sivaranjini S

Non-Maximum Suppression (NMS) is applied to the RPN's proposals before they are passed to the detection head, in order to reduce redundancy among highly overlapping proposals.

DenseNet differs from ResNet in that DenseNet concatenates feature maps from all preceding layers within a dense block, whereas ResNet adds the input of a block to its output.

In BPTT, gradients are computed only for the final time step.

Transposed convolution is used to reduce spatial dimensions in segmentation networks

In GANs, the generator and discriminator are trained simultaneously in a competitive manner.

FPN relies on multiple resized input images like traditional image pyramids.

Skip connections increase the number of learnable parameters significantly.

Two-stage detectors first classify objects and then generate region proposals.

A predictive model trained on time-stamped patient data without temporal validation can overfit by leaking future information into the training set.

The GAN generator is trained without ever directly seeing real data — it learns only via gradient signals back-propagated through the discriminator.

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