In this video, I will show you the most common activation functions used in deep learning with TensorFlow. Activation functions are used to introduce non-linearity into neural networks, which allows them to learn more complex relationships between inputs and outputs.

The activation functions I will cover in this video are:

  • Sigmoid: The sigmoid function is a classic activation function that is often used in classification problems.
  • Tanh: The tanh function is similar to the sigmoid function, but it has a wider range of outputs.
  • ReLU: The ReLU function is a newer activation function that is becoming increasingly popular due to its simplicity and efficiency.
  • Leaky ReLU: The leaky ReLU function is a variant of the ReLU function that addresses the problem of ReLU units "dying."
  • ELU: The ELU function is another variant of the ReLU function that has been shown to improve performance on some tasks.

I will also show you how to implement these activation functions in TensorFlow.

I hope this video helps you to understand the most common activation functions used in deep learning. If you have any questions, please leave a comment below.

Here are some additional tips for using activation functions in deep learning:

  • The choice of activation function can have a significant impact on the performance of a neural network.
  • It is important to experiment with different activation functions to find the ones that work best for your specific problem.
  • You can also combine different activation functions in a single neural network.

I hope this helps!


TensorFlow: Activation functions in deep learning
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