Activation function progress in deep learning, Relu, Elu, Selu, Geli , mish, etc – include table and graphs – day 24

Activation Function Formula Comparison Why (Problem and Solution) Mathematical Explanation and Proof Sigmoid σ(z) = 1 / (1 + e-z) – Non-zero-centered output – Saturates for large values, leading to vanishing gradients Problem: Vanishing gradients for large positive or negative inputs, slowing down learning in deep networks. Solution: ReLU was introduced to avoid the saturation issue by having a linear response for positive values. The gradient of the sigmoid function is σ'(z) = σ(z)(1 – σ(z)). As z moves far from zero (either positive or negative), σ(z) approaches 1 or 0, causing σ'(z) to approach 0, leading to very small...

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166

Activation Function _ day 11

Activation Functions in Neural Networks Activation Functions in Neural Networks: Why They Matter ? Activation functions are pivotal in neural networks, transforming the input of each neuron to its output signal, thus determining the neuron’s activation level. This process allows neural networks to handle tasks such as image recognition and language processing effectively. The Role of Different Activation Functions Neural networks employ distinct activation functions in their inner and outer layers, customized to the specific requirements of the network: Inner Layers: Functions like ReLU (Rectified Linear Unit) introduce necessary non-linearity, allowing the network to learn complex patterns in the data....

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