📌 Activation Functions Summary
Activation functions are mathematical formulas used in neural networks to decide whether a neuron should be activated or not. They help the network learn complex patterns by introducing non-linearity, allowing it to solve more complicated problems than a simple linear system could handle. Without activation functions, neural networks would not be able to model tasks like image or speech recognition effectively.
🙋🏻♂️ Explain Activation Functions Simply
Imagine a light switch that only turns on if enough power is supplied, but can also adjust how bright the light is. Activation functions in a neural network work like these switches, helping each unit decide how strongly to pass information along. This lets the network make smarter decisions, instead of just adding up numbers.
📅 How Can it be used?
Activation functions can be used in a machine learning model to recognise handwritten numbers from scanned forms.
🗺️ Real World Examples
In a voice assistant app, activation functions in the neural network help the system distinguish between different spoken words, enabling accurate voice recognition even with background noise.
In medical image analysis, activation functions enable neural networks to identify tumours in X-ray images by learning the subtle differences between healthy and abnormal tissues.
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