Deep Learning - Artificial Neural Networks with Tensorflow - Activation Functions

The video tutorial explores the sigmoid and tanH functions, highlighting their roles in neural networks and their limitations, such as the vanishing gradient problem. It introduces ReLU as a more effective activation function, discussing its advantages and biological plausibility. The tutorial emphasizes the importance of experimentation over theoretical beauty in machine learning.

What is the primary purpose of the sigmoid function in neural networks?

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What are the limitations of the sigmoid function as discussed in the text?

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Explain the significance of standardization in the context of neural networks.

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What is the relationship between the tan H function and the sigmoid function?

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Describe the vanishing gradient problem and its implications for deep neural networks.

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How does the rel U activation function differ from the sigmoid and tan H functions?

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What are dead neurons, and how do they affect neural network training?

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What are some alternative activation functions mentioned in the text, and what are their advantages?

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Why is experimentation emphasized over philosophy in machine learning according to the text?

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Discuss the concept of frequency coding in relation to the rel U activation function.

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