##### Asked by: Drusilla Barragan

asked in category: General Last Updated: 11th April, 2020# What are activation functions in deep learning?

**Neural network activation functions**are a crucial component of

**deep learning**.

**Activation functions**determine the output of a

**deep learning**model, its accuracy, and also the computational efficiency of training a model—which can make or break a large scale

**neural network**.

In this way, what are activation functions in machine learning?

Definition of **activation function**:- **Activation function** decides, whether a neuron should be **activated** or not by calculating weighted sum and further adding bias with it. The purpose of the **activation function** is to introduce non-linearity into the output of a neuron.

what are the types of activation function? **Popular types of activation functions and when to use them**

- Binary Step Function.
- Linear Function.
- Sigmoid.
- Tanh.
- ReLU.
- Leaky ReLU.
- Parameterised ReLU.
- Exponential Linear Unit.

Subsequently, question is, what is the activation function used for?

**Most popular types of Activation functions -**

- Sigmoid or Logistic.
- Tanh — Hyperbolic tangent.
- ReLu -Rectified linear units.

What is meant by activation function in neural network?

In artificial **neural networks**, the **activation function** of a node defines the output of that node given an input or set of inputs. A standard integrated circuit can be seen as a digital **network** of **activation functions** that can be "ON" (1) or "OFF" (0), depending on input.