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Beyond the Curve: The Magic of Sigmoid + Code
- The AND gate is a basic digital logic gate that implements logical conjunction (∧) from mathematical logic.
- The activation function in an artificial neural network (ANN) plays a critical role in determining the output of a neuron.
- Activation functions like ReLU, Sigmoid, and Tanh introduce non-linearities, enabling the network to learn complex patterns.
- Activation functions like Sigmoid and Tanh are differentiable, meaning that their derivatives can be used in backpropagation to update the network’s weights during training.
- The sigmoid function is one of the most well-known and widely used for classification tasks.
- Sigmoid function is especially valuable in binary classification tasks, where we predict between two classes (e.g., yes/no, 0/1).
- The sigmoid function as we can see is an s-shaped curve. For any value of x the sigmoid function will output a value between 0 and 1.
- This compression property makes sigmoid useful for models that require output in a [0, 1] range, especially for binary classification.
- While sigmoid can work well as an output activation in binary classification, it’s generally not recommended for hidden layers in deep networks.
- For hidden layers, the hyperbolic tangent (tanh) function is often preferred over sigmoid.
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