ReLU non-linear activation function explained in 1min #deeplearning

This is a very simple ReLU activation function explanation for AI programming beginners who are new to deep learning and the components of neural networks.
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Rectified Linear Unit or ReLU is a standard activation function in Deep Learning.

It adds non-linearity to the model, which helps neural networks learn complex patterns.

The ReLU function definition is:

f(x) = max(0, x)

Suppose x is the pre-activation value of a neuron;

That value is passed to the ReLU function.

This produces f(x).

If x is a positive value, it stays as is.

If x is a negative value, it becomes 0.

Therefore, a neuron only activates if its input is positive.

The f(x) output feeds into the neural network's next layer.

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Видео ReLU non-linear activation function explained in 1min #deeplearning канала Neural Encoding