A naukri.com initiative
Medium
4d
65
Image Credit: Medium
Your Cat’s Guide to Activation Functions in Neural Networks
- Neurons in neural networks perform a weighted sum of inputs to calculate an output, which is then sent to another neuron.
- Artificial neurons have two main properties: weight and bias, and they perform a linear transformation on inputs.
- An activation function is used to transform the output of neurons, making the network capable of handling non-linear processes.
- Common activation functions include Rectified Linear Unit (ReLU), Sigmoid, Softmax, and Hyperbolic Tangent (tanh).
- ReLU is preferred for its simplicity and ability to handle large input values effectively.
- Sigmoid is useful for binary classification tasks by mapping inputs to values between 0 and 1.
- Softmax normalizes a vector of real numbers into a probability distribution, crucial for multi-class classification.
- Hyperbolic Tangent (tanh) is similar to sigmoid but outputs values between -1 and 1, aiding in gradient descent optimization.
- Binary Step function is a basic threshold-based activation function used in simple classification tasks.
- Bias in neurons allows for shifting the activation function curve, providing flexibility in fitting data and improving network performance.
Read Full Article
3 Likes
For uninterrupted reading, download the app