Neural Networks with TensorFlow

Kursinhalt

Neural Networks with TensorFlow

Neural Networks with TensorFlow

1. Basics of Keras

Layers Model Creation Model Compilation Data Preprocessing Model Training and Evaluation Challenge Model Save and Load Early Stopping and Checkpoints Hyperparameter Tuning Challenge Quiz

2. Regularization

Overfitting and Underfitting What is Regularization?L1, L2 Regularization Dropout Batch Normalization Challenge Quiz

3. Advanced Techniques

Optimizers Learning Rate Scheduling TensorFlow Datasets Data Generators Non-Sequential Models Transfer Learning Multitask Learning Challenge Quiz Summary

Quiz

1. Which optimizer is known for combining the benefits of both Momentum and RMSprop?

2. In multitask learning, how does sharing lower layers of a neural network benefit the model?

3. How does using the prefetch transformation in `tf.data.Dataset` benefit training performance?

4. How does an exponential decay learning rate scheduler calculate the learning rate during training?

5. How does fine-tuning work in transfer learning?

6. How does the Momentum optimizer help in overcoming local minima?

7. Why is transfer learning particularly beneficial in domains with limited training data?

8. How does the RMSprop optimizer address the diminishing learning rates problem encountered in AdaGrad?

Which optimizer is known for combining the benefits of both Momentum and RMSprop?

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Adam

SGD

AdaGrad

Nadam

In multitask learning, how does sharing lower layers of a neural network benefit the model?

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Increases computational cost.

Reduces model capacity.

Enables the model to generalize across tasks.

How does using the prefetch transformation in tf.data.Dataset benefit training performance?

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It allows for the loading of batches concurrently with the processing of the current data.

It prefetches data to GPU memory for faster access.

It increases the size of the dataset.

It reduces the need for shuffling.

How does an exponential decay learning rate scheduler calculate the learning rate during training?

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By increasing the learning rate after a certain number of epochs.

By keeping the learning rate constant throughout training.

By reducing the learning rate based on a predefined function.

By randomly adjusting the learning rate at each epoch.

How does fine-tuning work in transfer learning?

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Fine-tuning completely replaces a part of a pre-trained model with a new model built from scratch.

Fine-tuning involves unfreezing some of the pre-trained model's layers and training them along with a new classifier on the new dataset.

Fine-tuning only trains the first few layers of the pre-trained model while keeping the rest frozen.

How does the Momentum optimizer help in overcoming local minima?

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It prevents the optimizer from making any updates to the model weights.

It helps in accelerating the optimizer in the right direction and dampens oscillations.

Momentum makes the learning rate increase exponentially.

It keeps the learning rate constant, independent of the gradient.

Why is transfer learning particularly beneficial in domains with limited training data?

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It allows the model to leverage knowledge from pre-trained models.

Transfer learning only works with unlimited data.

It guarantees high accuracy on any task.

How does the RMSprop optimizer address the diminishing learning rates problem encountered in AdaGrad?

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By resetting the learning rate at each epoch.

By implementing a constant learning rate throughout the training.

By using a moving average of squared gradients to adjust the learning rate.

RMSprop does not address the diminishing learning rates problem.

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Abschnitt 3. Kapitel 9

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