Neural networks are systems that operate similarly to the human brain. Currently, neural networks are used daily - even without us realizing it. For example, when we use our mobile assistant to perform a search- such as Siri, Google, or Amazon Web- it is all neural-network driven. In computer games, neural networks are also utilized - such as how the system adjusts its players, maps applications, processes map images, and finds the shortest routes to reach the destination.
Neural networks can perform any of the following tasks:
Translate text
Control robots
Recognize speech
Read handwritten text
Identify faces
How does the Neural Network Function?
There are three different layers to neural networks:
Input layer: Picks up the input signals and passes them on to the subsequent layers.
Hidden layer: This layer does every feature extraction and calculation required. Often, we will find more than one hidden layer in a single neural network.
Output layer: This is the final layer and has the job of delivering the result
To better understand these layers and their workings, let’s take a real-life example of how traffic cameras capture the license plates of vehicles speeding on the road. Pixels, which make up the picture, are fed into the input layer in arrays. Each neuron has activation, or an assigned number, ranging from 0 to 1. For the white pixel, it is 1, and for the black, it is 0. Next, the input layer passes these inputs to the hidden layer. All the interconnections have weights assigned to them randomly, which then is multiplied by the input signal. Lastly, a bias is added to all these weights.
The weighted addition of the inputs is then converted to the information of the activation function- this function decides which nodes should fire for the feature extraction.
Here we will take a look at the different types of activation functions that are there:
Threshold function: This function is deployed when we don’t want to worry about mid-level uncertainty.
Sigmoid function: The sigmoid function is useful when the model is required to predict probability.
Hyperbolic Tangent function: This function is very similar to the sigmoid function; however, it ranges from -1 to 1.
ReLU (rectified linear unit) function: This rectified linear unit (ReLU) function works in such a manner that it gives the function value, however, if the value is over 1, then it will just be one, and similarly, if the value is less than 0, it will be just 0. This ReLU function is the most commonly used among all.
Now that we know all the activation function types, let’s explore more neural networks. So finally, the model, after having applied a suitable application function to the output layer, will predict what the outcome is. The error rate in the output is minimized through the adjustment of weights, and the error itself is back-propagated through the network. The weights are constantly adjusted until they perfectly fit all the training models that it has been put in. Furthermore, the output is contrasted with the original result, and numerous iterations are done to attain maximum accuracy.
Neural Networks and their Types
There are mainly six different types of neural networks, as discussed below:
Feed-forward neural network: This is the example we just explored where data travels in a uni-directional manner- from input to output. It is also the simplest form of artificial neural network.
Radial basis function neural network: Here, the data point is classified based on distance from any central point. We often want to group things and contract a centre point when we don’t have any other training data. This network groups similar data points and has its application in power restoration systems.
Kohonen Self-organizing Neural Network: Discrete maps that are comprised of neurons have inputs that are vectors of random input. Applications of this network include recognizing patterns in data such as medical analysis.
Recurrent Neural Network: Here, the hidden layer saves the output such that it becomes part of the new output and can be used for future predictions.
Convolution Neural Network: Here, the input features are filtered and taken in batches, therefore, allowing the network to remember an image.
Modular Neural Network: This model consists of multiple neural networks which function together to get an output.
Keras is an efficient software that allows users to create artificial neural networks with the Python programming language. Many data scientists use Python because it is essential for the Keras software. If you are new to Python and data science, you can opt for a Data Science and Analytics course or data analyst training to learn how to use Python more efficiently.
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