# Machine Learning Activation Function in Neural Network

Machine learning, and especially deep learning, are two technologies that are changing the world.

After a long “AI winter” that spanned 30 years, computing power and data sets have finally caught up to the artificial intelligence algorithms that were proposed during the second half of the twentieth century.

This means that deep learning models are finally being used to make effective predictions that solve real-world problems.

It’s more important than ever for data scientists and software engineers to have a high-level understanding of how deep learning models work. This article will explain the history and basic concepts of deep learning neural networks in plain English.
Here is a simplified visualization to demonstrate how this works:

Neural nets represented an immense stride forward in the field of deep learning.

However, it took decades for machine learning (and especially deep learning) to gain prominence.

We’ll explore why in the next section.

# Weight :

Weights are a very important topic in the field of deep learning because adjusting a model’s weights is the primary way through which deep learning models are trained. You’ll see this in practice later on when we build our first neural networks from scratch.

Once a neuron receives its inputs from the neurons in the preceding layer of the model, it adds up each signal multiplied by its corresponding weight and passes them on to an activation function

# Bias :

Bias terms are additional constants attached to neurons and added to the weighted input before the activation function is applied. Bias terms help models represent patterns that do not necessarily pass through the origin.

# 1.Binary Step Function :

This activation function very basic and it comes to mind every time if we try to bound output. It is basically a threshold base classifier, in this, we decide some threshold value to decide output that neuron should be activated or deactivated.

In this, we decide the threshold value to 0. It is very simple and useful to classify binary problems or classifier.

# 2.Linear Activation Function :

It is a simple straight line activation function where our function is directly proportional to the weighted sum of neurons or input. Linear activation functions are better in giving a wide range of activations and a line of a positive slope may increase the firing rate as the input rate increases.

In binary, either a neuron is firing or not. If you know gradient descent in deep learning then you would notice that in this function derivative is constant.

Y = mZ

Where derivative with respect to Z is constant m. The meaning gradient is also constant and it has nothing to do with Z. In this, if the changes made in backpropagation will be constant and not dependent on Z so this will not be good for learning.

In this, our second layer is the output of a linear function of previous layers input. Wait a minute, what have we learned in this that if we compare our all the layers and remove all the layers except the first and last then also we can only get an output which is a linear function of the first layer.

# 3.Sigmoid Activation Function :

The sigmoid activation function is used mostly as it does its task with great efficiency, it basically is a probabilistic approach towards decision making and ranges in between 0 to 1, so when we have to make a decision or to predict an output we use this activation function because of the range is the minimum, therefore, prediction would be more accurate.

The equation for the sigmoid function is

f(x) = 1/(1+e(-x) )

The sigmoid function causes a problem mainly termed as vanishing gradient problem which occurs because we convert large input in between the range of 0 to 1 and therefore their derivatives become much smaller which does not give satisfactory output. To solve this problem another activation function such as ReLU is used where we do not have a small derivative problem.

# 4.Tanh Function :

This activation function is slightly better than the sigmoid function, like the sigmoid function it is also used to predict or to differentiate between two classes but it maps the negative input into negative quantity only and ranges in between -1 to 1.

# 5.Relu ( Rectified Linear unit) Function :

Rectified linear unit or ReLU is most widely used activation function right now which ranges from 0 to infinity, All the negative values are converted into zero, and this conversion rate is so fast that neither it can map nor fit into data properly which creates a problem, but where there is a problem there is a solution.

We use Leaky ReLU function instead of ReLU to avoid this unfitting, in Leaky ReLU range is expanded which enhances the performance.

# 6. Softmax Function :

Softmax is used mainly at the last layer i.e output layer for decision making the same as sigmoid activation works, the softmax basically gives value to the input variable according to their weight and the sum of these weights is eventually one.

For Binary classification, both sigmoid, as well as softmax, are equally approachable but in case of multi-class classification problem we generally use softmax and cross-entropy along with it.

# Conclusion :

The activation functions are those significant functions that perform a non-linear transformation to the input and making it proficient to understand and executes more complex tasks. We have discussed 7 majorly used activation functions with their limitation (if any), these activation functions are used for the same purpose but in different conditions.

# Resource:

student at holbertonschool

## More from Hamdi Ghorbel

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