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An Introduction to Feedforward Neural Network: Layers, Functions & Importance

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An Introduction to Feedforward Neural Network: Layers, Functions & Importance

Deep learning technology is the backbone of search engines, machine translation, and mobile applications. It works by imitating the human brain to find and create patterns from different kinds of data.

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One important part of this incredible technology is a feedforward neural network, which assists software engineers in pattern recognition and classification, non-linear regression, and function approximation.

Let’s get some insights into this essential aspect of the core neural network architecture.

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What is Feedforward Neural Network?

Commonly known as a multi-layered network of neurons, feedforward neural networks are called so due to the fact that all the information travels only in the forward direction.

The information first enters the input nodes, moves through the hidden layers, and finally comes out through the output nodes. The network contains no connections to feed the information coming out at the output node back into the network.

Feedforward neural networks are meant to approximate functions.

Here’s how it works.

There is a classifier y = f*(x).

This feeds input x into category y. 

The feedforward network will map y = f (x; θ). It then memorizes the value of θ that approximates the function the best.

Feedforward neural network for the base for object recognition in images, as you can spot in the Google Photos app. 

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The Layers of a Feedforward Neural Network

A feedforward neural network consists of the following.

Input layer

It contains the input-receiving neurons. They then pass the input to the next layer. The total number of neurons in the input layer is equal to the attributes in the dataset.

Hidden layer

This is the middle layer, hidden between the input and output layers. There is a huge number of neurons in this layer that apply transformations to the inputs. They then pass it on to the output layer.

Output layer

It is the last layer and is dependent upon the built of the model. Also, the output layer is the predicted feature as you know what you want the result to be.

Neuron weights

The strength of a connection between the neurons is called weights. The value of a weight ranges 0 to 1.

Know more: Neural Network Model: Brief Introduction, Glossary

Cost Function in Feedforward Neural Network

Choosing the cost function is one of the most important parts of a feedforward neural network. Usually, small changes in weights and biases don’t affect the classified data points. So, to figure out a way to improve performance by using a smooth cost function to make small changes to weights and biases. 

The formula for the mean square error cost function is:



w = collection of weights in the network

b = biases

n = number of training inputs

a = output vectors

x = input

‖v‖ = usual length of vector v


Loss Function in Feedforward Neural Network

The loss function in the neural network is meant for determining if there is any correction the learning process needs.

The output layer neurons will be equal to the number of classes. To compare the difference between predicted and true probability distribution.

Cross-entropy loss for binary classification is:

Cross-entropy loss for multi-class classification is:


Gradient Learning Algorithm

This algorithm helps determine all the best possible values for parameters to diminish the loss in the feedforward neural network. 


All the weights (w₁₁₁, w₁₁₂,…) and biases b (b₁, b₂,….) are initialized randomly. Once this is done, the observations in the data are iterated. Then, the corresponding predicted distribution is determined against each observation. Finally, the loss is computed using the cross-entropy function. 

The loss value then helps figure the changes to make in weights to decrease the overall loss of the model. 

Read: 13 Interesting Neural Network Project Ideas & Topics

The Need for a Neuron Model

Let’s say the inputs being fed into the network are raw pixel data that comes from a scanned image of a character. For the output in the network to classify the digit correctly, you would want to determine the right amount of weights and biases.

Now, you would need to make small changes to the weight in the network see how the learning would work. For this to turn out perfectly, small changes in the weights should only lead to small changes in the output. 

However, what if the small change in the weight amounts to a big change in the output? The sigmoid neuron model can solve such an issue.

Also Read: The 7 Types of Artificial Neural Networks ML Engineers Need


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Deep learning is a territory of software engineering with a colossal extent of research. There are a lot of neural network architectures actualized for various data types. Convolutional neural systems, for instance, have accomplished best-in-class execution in the fields of image handling procedures, while recurrent neural systems are generally utilized in content and voice processing. 

At the point when applied to huge datasets, neural systems need monstrous measures of computational force and equipment acceleration, which can be accomplished through the design of arranging graphics processing units or GPUs. On the off chance that you are new to utilizing GPUs, you can discover free configured settings on the web. The most preferred ones are Kaggle Notebooks or Google Collab Notebooks. 

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To accomplish an effective feedforward neural network, you perform several iterations in the network architecture, which needs a great deal of testing.

For more information on how these networks work, learn from the experts at upGrad. Our courses are incredibly comprehensive, and you can resolve your queries by directly getting in touch with our experienced and best-in-class teachers.

If you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.


Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
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Frequently Asked Questions (FAQs)

1Is linear algebra required in neural networks?

A neural network is a mathematical model that solves any complex problem. The network takes a set of inputs and calculates a set of outputs with the goal of achieving the desired outcome. When studying neural network theory, the majority of the neurons and layers are frequently formatted in linear algebra. Linear algebra is necessary to construct the mathematical model. You may also use linear algebra to comprehend the model's networking. Thus, to answer the question, yes, the basic knowledge of linear algebra is mandatory while using neural networks.

2What is meant by backpropagation in neural networks?

In the case of neural networks that employ gradient descent, backpropagation is used. This algorithm estimates the gradient of the error function with respect to the neural network's weights and is essentially a backward propagation of mistakes. This approach is employed because fine-tuning the weights reduces error rates and thus improves the generalization of the neural network model, making it more dependable. Backpropagation is commonly categorized as a form of supervised machine learning since it requires a known, intended result for each input value in order to compute the loss function gradient in neural networks.

3How is backpropagation different from optimizers?

In neural networks, both optimizers and the backpropagation algorithm are used, and they work together to make the model more dependable. Backpropagation is used to efficiently calculate gradients, and optimizers are used to train the neural network using the gradients obtained using backpropagation. In a nutshell, what backpropagation does for us is compute gradients, which are subsequently used by optimizers.

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