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Deep Learning Fundamentals

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    Deep Learning Fundamentals > Unit 6 > Unit 6.7
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    6.7 Reducing Overfitting With Dropout

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    References

    https://arxiv.org/abs/1207.0580

    What we covered in this video lecture

    The lecture delved into the concept of dropout as a regularization technique for neural networks, aimed at mitigating overfitting and improving generalization. By randomly dropping out neurons during training with a certain probability, the network is forced to learn more robust features, preventing reliance on any single neuron.

    Additional resources if you want to learn more

    If you want to learn more about Dropout, I recommend checking out the classic A Simple Way to Prevent Neural Networks from Overfitting paper.

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    Quiz: 6.7 Reducing Overfitting With Dropout - Part 1

    A drop probability of 0.5 in a dropout layer means that we are dropping 50% of the

    Correct. Note that the activations are not dropped directly, but if we use ReLU activations, it has the same effect. That’s because an input of zero results in an output of zero. However, for a sigmoid activation, an activation of zero results in an output of 0.5.

    Incorrect. The activations are not dropped directly. If we use ReLU, an input of zero results in an output of zero. However, for a sigmoid activation, an activation of zero results in an output of 0.5.

    Incorrect. One dropout layer only affects one hidden layer.

    Please answer all questions to proceed.

    Quiz: 6.7 Reducing Overfitting With Dropout - Part 2

    If we use dropout with a drop probability of 0.2 during inference, it will drop

    Correct. During inference, no nodes are dropped.

    Incorrect. During inference, no nodes are dropped.

    Incorrect. During inference, no nodes are dropped.

    Incorrect. During inference, no nodes are dropped.

    Please answer all questions to proceed.

    Quiz: 6.7 Reducing Overfitting With Dropout - Part 3

    To enable the deterministic behavior of Dropout layers during inference mode, we

    Incorrect. While this is recommended for running a model during inference to save memory, it does not enable the deterministic behavior of dropout layers.

    Incorrect. While this is recommended for running a model during inference to save memory, it does not enable the deterministic behavior of dropout layers.

    Correct. However, in addition, you should also use torch.no_grad or torch.inference_mode()

    Please answer all questions to proceed.
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    Unit 6.7

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