What is Feedforward Neural Network and How is it Useful?

Artificial intelligence is rapidly changing the world as we know it. From composing our emails, building captivating resumes, and writing codes to enhancing safety measures in cars, improving cybersecurity, and building smart infrastructures—AI is omnipresent. But what are the components that enable AI to achieve such a wonderful feat? Along with a few other elements, neural networks are among the key constituents of AI technology. In a sense, it won’t be far-fetched to claim that neural networks are at the heart of AI. A feedforward neural network is one of the most primary networks used in machine learning. But what exactly is a feedforward neural network? How does it work? Moreover, what are its applications and what kind of advantages does it grant us? Let’s find out.  

Understanding Feedforward Neural Network

Feedforward neural network is one of the first artificial neural networks. It is considered to be one of the most simple neural networks. Here, the flow of information through the network is strictly unidirectional. Here, information goes from the input layer, through the hidden layers (if there are any), to the output layer. The architecture of a feedforward network does not contain any cyclic or recursive connections. This feature distinguishes it from other complex networks such as recurrent neural networks, or convolutional neural networks.

Architecture of a Feedforward Neural Network

There are three primary layers in a feedforward neural network. These are:  

1. The input layer, 
2. Hidden layers
3. The output layer. 

These layers consist of neurons, and these neurons are connected through a series of weights.

A. The Input Layer

The layer serves as the entry point for data, with its neuron count reflecting the dimensions of the input. Neurons in this layer relay the input data to the subsequent layer.

B. The Hidden Layer

The hidden layers—the computational root of the feedforward network— is tasked with intricate processing. To elaborate, neurons in these layers compute weighted sums of the inputs received from the previous layer. After that, the hidden layers perform an activation function. Interestingly, activation functions in neural networks fall into three main categories: Sigmoid, Tanh, and Rectified Linear Unit (ReLU).

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• The Sigmoid function transforms input values so that they fall within a range of 0 to 1
• The Tanh function maps input values to a range between -1 and 1
• The Rectified Linear Unit (ReLU) function allows only positive values to pass through, converting any negative input values to 0

Subsequently, the output is passed on to the next layer. Whether there would be one or more layers would depend on the configuration of the network.  

C. The Output Layer

This is the final layer of a feedforward neural network. Here, the network produces its output based on the input data. The number of neurons in this layer aligns with the number of possible outputs the network is designed to generate.

How Does a Feedforward Neural Network Function?

To understand how a feedforward neural network works, let’s look at a very simple example. Please note that this is an extremely simple example of the functioning of a feedforward network. In contrast, its real life applications can be way more complex. 

So, here is an example that uses a feedforward neural network to predict whether a student will pass or fail in her exam. This will be based on three factors: hours studied (x1), class attendance (x2), and sleep hours (x3). 

Now, assume that the network’s initial weights are w1 = 0.5, w2 = 1.5, and w3 = 1, with biases set to 1, 0, and 2, respectively.

Step 1: From Forward Pass to Output

First, the network multiplies each input by its corresponding weight:

(x1 * w1) = (5 * 0.5) = 2.5

(x2 * w2) = (80 * 1.5) = 120

(x3 * w3) = (6 * 1) = 6

Next, it adds the biases:

(2.5 + 1) = 3.5

(120 + 0) = 120

(6 + 2) = 8

The network then sums these values: 3.5 + 120 + 8 = 131.5. This weighted sum is passed through an activation function to make the final prediction. If the output is greater than 15, the network predicts that the student will pass.

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Step 2: Calculating the Error

Suppose, in reality, the student failed her exam, proving that the prediction was wrong. In such a scenario, the network would attempt to calculate the error by comparing its prediction to this actual result. Simply put, this error signals how far off the prediction was. It is here that backpropagation and gradient descent comes into play. 

Step 3: Backpropagation and Gradient Descent

Backpropagation begins by sending this error backward through the network. Consequently, the network calculates how each weight contributed to the error. 

Now, using gradient descent, the network then adjusts the weights to reduce this error. For instance, if w1 was too high, gradient descent reduces it slightly. This process repeats across many epochs, with the network continuously refining the weights.

Through this iterative process of forward feeding, backpropagation, and gradient descent, the network learns and improves its predictions, becoming more accurate over time.

What are the Applications of Feedforward Neural Network?

Feedforward network is used for performing a diverse category of tasks, such as— 

• Computer Vision: Feedforward networks process visual data for self-driving cars, content moderation, manufacturing defect detection, and image search
• Natural Language Processing (NLP): They enable AI assistants, virtual agents, sentiment analysis, and accurate language translation
• Time Series Forecasting: Feedforward networks predict stock market trends, weather patterns, and retail sales fluctuations

Advantages and Limitations of Feedforward Neural Networks

Feedforward neural networks offer several advantages. For example, these networks are—

• Relatively straightforward architecture compared to other neural network types
• Computationally efficient for certain tasks
• Often function as the building block for more complex architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)

Despite the advantages stated above, a feedforward neural network comes with its own set of limitations. For instance, in this type of network, determining the optimal number of hidden layers and neurons can be challenging. This can be detrimental to the network’s overall performance. Furthermore, feedforward networks are known for having a dispensation for overfitting. It means that during the training phase, the network often ends up learning the training data a little too well. It even learns the noises. Thus, while it works well while operating on training data, its performance becomes sub par when faced with new data. 

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So, do you find yourself drawn to the captivating world of neural networks and their potential in AI? If you’re excited to advance your AI and machine learning knowledge, consider joining Emeritus’ artificial intelligence and machine learning courses. These courses, taught by industry experts, are made available to you by Emeritus. They provide in-depth training in neural networks and other key AI tools. So, stop procrastinating and start your journey with Emeritus. Prepare yourself for the AI-dominated future of the Indian job market. 

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