What are Activation Functions Neural Networks | Explain Its Types

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A neural network is a complex system inspired by the brain, and you're building it. One important decision you must make is how the information will flow within the network. This article explains your multiple choices for a key part called the "activation function neural networks" that helps with this information flow.

What Are the Activation Functions Neural Networks?

A neural network is a super-powered learning machine. Activation function neural network are special tools inside this machine. They take simple information, process it, and turn it into something the machine can use to learn complex things from data. Without them, the machine would only be good at learning basic patterns. Activation functions make the machine much more powerful.

This article will explain the different activation functions and how to choose the right one for your situation. Whether you're new to this field or already a pro, understanding these functions will give you a better sense of how neural networks work and make you better at using them.

Types of Activation Function in Neural Network

The following section will discuss different types of activation functions neural networks. Read and understand each of them.

Linear Activation Functions

Linear activation functions are the basic filters in a neural network. They process information in a straight-line way, which is easy to understand but limits what the network can learn. As a result, this makes them unsuitable for complex tasks because they can't capture the intricate relationships between data points. Additionally, they induce problems during training, making it difficult for the network to learn effectively. So, while they're simple, linear activation functions aren't very powerful for building powerful neural networks.

Non-Linear Activation Functions

Unlike linear functions (straight lines), they allow the network to learn complex patterns in data. However, this is because they can handle more intricate relationships between the information.
 

• Sigmoid and tanh: These functions squeeze the data between specific ranges (0 to 1 or -1 to 1). They're practical for some tasks but can include problems with learning.
• ReLU: This is a popular choice that avoids some problems of sigmoid and tanh. It basically sets negative values to zero and keeps positive values the same.
  • Leaky ReLU: A variation of ReLU that allows a small negative slope for negative values, helping to prevent inactive neurons.
  • Parametric ReLU: Gives neurons more flexibility in how they handle negative inputs.
     
• Maxout: A more complex function based on ReLU, but requires training more parameters.
• ELU: Similar to ReLU but smoother for negative inputs, aiming for faster learning and better accuracy.

Softmax is a unique type of activation function. It uses for tasks where you want the neural network to predict probabilities, for example, showing the machine pictures of cats and dogs. 

Elements of Activation Function for Neural Network
 

Here, we will discuss several elements of activation functions neural networks. 
 

• Input layer: This is the first stop for data, like showing a picture of a handwritten digit.
• Hidden layers: It is the secret sauce, like the brain's thinking center, where the learning patterns happen. More hidden layers mean the network can learn extra complex things.
• Output layer: It is where the answer comes out, like telling you which digit the picture is most likely to be.
• Neurons: This is tiny processing unit inside the network, like tiny calculators. They connect with pathways including unique values called weights. These weights influence how much one neuron's information affects another.

Merits and Demerits of Activation Functions for Neural Network

These functions take the information flowing through a neural network and add complexity. As a result, they allow the network to learn more interesting things. The following will discuss the pros and then elaborate on the cons.

Pros:
 

• Linear: Good range of outputs, can combine signals from many neurons.
• Sigmoid: Handles complex data, and gives smooth outputs.
• Tanh: Stronger learner than Sigmoid, tackles vanishing gradients a bit better.
• ReLU: Fast learner, avoids vanishing gradients.
• Leaky ReLU: Fixes the "dead neuron" problem of ReLU (partially).
• ELU: Generally faster learning and better accuracy than ReLU. 

Cons: 
 

• Limited learning power because it's too simple.
• Slow learning due to vanishing gradients.
• It can still struggle with vanishing gradients.
• Activation Functions Neural Networks can cause some neurons to become inactive ("dead neurons"). Only use in hidden layers.
• They might not be complex enough for certain tasks.
• Produce unexpected outputs and might have a wider output range than desired.

Activation Function in Neural Network Example
 

• This is a regression problem, where the network learns to predict a continuous value (the price).
• Since house prices can be any value, immense or small, we can use a linear activation function in the final layer. As a result, this lets the network output any number on that scale.
• However, even with a linear output, the hidden layers in the neural network still need non-linear activation functions. These hidden layers are where the network learns the complex patterns that influence house prices.

Conclusion

Activation functions neural networks take simple information and process it in a way that allows the network to learn complex patterns from data. Without them, neural networks would be stuck in a straight-line way of thinking, unable to capture the intricate relationships between things. Choosing the right activation function depends on the task at hand, but some, like ReLU and its variations, are popular for their efficiency and ability to avoid getting stuck. So, the next time you hear about neural networks, remember the tiny activation functions working tirelessly and secretly to help them learn amazing things.

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