Blog_Banner_Asset
    Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconArtificial Intelligencebreadcumb forward arrow iconNeural Network Model: Brief Introduction, Glossary & Backpropagation

Neural Network Model: Brief Introduction, Glossary & Backpropagation

Last updated:
22nd May, 2020
Views
Read Time
7 Mins
share image icon
In this article
Chevron in toc
View All
Neural Network Model: Brief Introduction, Glossary & Backpropagation

If you’re a software engineering enthusiast trying to get an understanding of how neural networks work, you’re in the right place.

Best Machine Learning and AI Courses Online

In this guide, we will help beginners learn the meaning of neural networks, get an introduction on what a neural network model is, and expand their knowledge to new areas of the field.

What is a Neural Network?

Before getting into computational terminology, let us understand the existence of neural networks in our everyday lives.

Ads of upGrad blog

In-demand Machine Learning Skills

Get Machine Learning Certification from the World’s top Universities. Earn Masters, Executive PGP, or Advanced Certificate Programs to fast-track your career.

The term “neural” comes from “neuron”, which is the term used for a single nerve cell. That’s right – a neural network essentially means a network of neurons that perform simple and actions in our daily lives.

Pattern recognition, object detection, and intelligence are a major aspect of the problems we face every day. While they are performed with so much ease that we don’t even realize, the truth is that these reactions are difficult to automate.

Example:

  • Children memorizing what an apple looks like
  • An animal recognizing its mother or owner
  • Perceiving whether something is hot or cold

Our neural networks perform these complicated computations.

Humans have now been able to build a computation system that can perform in a manner similar to our nervous system. These are called artificial neural networks (ANNs). 

While we used the ANNs initially to perform simple functions, the increase in computation power has now allowed us to build a rather powerful neural network architecture to solve increasingly complicated problems.

Let’s learn about ANN in-depth in the next section.

Read: TensorFlow Object Detection Tutorial For Beginners

What is an Artificial Neural Network Model?

A multi-layer, fully-connected neural network containing an input layer, hidden layers, and an output layer is called an artificial neural network or ANN.

The image below represents an ANN.

Source

If you see carefully, you will notice that each node in one layer is connected to every node in the layer next to it.

As you increase the number of hidden layers, the network becomes deeper.

Let’s see what an individual node in the output or hidden layer looks like.

Source

As you can see, the node gets many inputs. It sums up all the weights and passes it on as output, via a non-linear activation function.

This output of the node becomes the input of the node in the next layer.

An important thing to note here is that the signal will always move from left to right. Once all the nodes have followed the procedure, the final output will be given.

Here’s what the equation of a node looks like.

Source

In the above equation, b is bias. It is the input to all nodes and always carries the value 1.

Bias helps to move the activation function result to left or right.

Glossary of Artificial Neural Network Model

Let’s look at the basic terms you should know when it comes to an artificial neural network model.

Inputs

The data first fed into the neural network from the source is called the input. Its goal is to give the network data to make a decision or prediction about the information fed into it. The neural network model usually accepts real value sets of inputs and it should be fed into a neuron in the input layer.

Training set

The inputs for which you already know the correct outputs are called training sets. These are used to help the neural network get trained and memorize the result for the given input set.

Outputs

Every neural network generates an output as a prediction or decision about the data fed into it. This output is in the form of real values set or Boolean decisions. Only of the neurons in the output layer generates the output value.

Neuron

Also known as a perceptron, a neuron is the basic unit of a neural network. It accepts an input value and generates an output based on it.

As discussed before, every neuron receives a part of the input and passes it through the non-linear activation function to the node in the next layer. These activation functions can be TanH, sigmoid, or ReLu. The non-linear feature of these functions helps to train the network.

Weight space

Every neuron has a numeric weight. When it delivers input to another note, its weight is totaled with the others to generate an output. By making small changes to these weights are how neural networks are trained. The fine-tuning of weights helps determine the correct set of weights and biases that would generate the best outcome. This is where backpropagation comes in.

What is Backpropagation in a Neural Network Model?

One of the ways to successfully find out the small changes that need to be made to the weights to minimize the loss of the entire network is backpropagation.

  • At first, the activations are to be propagated in the upward or feedforward direction.
  • Now, the cost function derivatives have to be propagated in the downward or reverse direction.

This way, you will be able to determine the partial cost derivative against each weight. You can then compute the cost that would be reduced by making the adjustments.

Popular AI and ML Blogs & Free Courses

Conclusion

Ads of upGrad blog

A lot of software engineers do not recommend the neural network model, as they feel that it is rather inefficient, given that several iterations are needed to come up with the most cost-effective solution. 

However, numerous new algorithms, such as Hinton’s capsule networks, capsule neural network, require much fewer instances of adjustments to reach a precise model. And so, surely neural networking has a lot of scope in the future.

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.

Profile

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.
Get Free Consultation

Selectcaret down icon
Select Area of interestcaret down icon
Select Work Experiencecaret down icon
By clicking 'Submit' you Agree to  
UpGrad's Terms & Conditions

Our Popular Machine Learning Course

Frequently Asked Questions (FAQs)

1Why do you need to study math for artificial intelligence?

Contrary to what many of us think, artificial intelligence is highly dependent on mathematics. The whole concept of teaching machines to think and act similar to human beings is based on concepts that belong to different branches of mathematics, like probability and statistics, to name a few. Data science also comes with its underpinnings related to various mathematical concepts, from calculus, linear algebra, and game theory to advanced regressions, classifications, statistics, probability, gradient descent, and more. Strong fundamentals in mathematics are essential for developing an effective understanding of AI concepts, which will help you build a successful career in this field.

2Is neural network an algorithm in machine learning?

Artificial neural networks or simply, neural networks, can be defined as a set of machine learning algorithms designed to resemble the human brain and created for advanced pattern recognition. Neural networks are designed to identify numerical patterns within vectors into which all the accumulated data are translated. By detecting patterns within unstructured or imprecise data, neural networks help to classify and form clusters for improved data storage and management. Sensory data that is fed to neural networks are interpreted via a machine perception that labels or clusters the input data.

3Why is backpropagation needed in neural networks?

In the realm of artificial neural networks, the backpropagation method actually refers to the backward propagation of errors. This is a standardized technique used to train artificial neural networks and goes by iterations. Backpropagation is used to fine-tune the weights of an artificial neural network, help minimize errors and make the system more accurate and reliable. This method works quickly and can also be programmed simply. It is a flexible technique; it does not need previous knowledge about the neural network. It involves no extra parameters but only tunes the numbers fed into the system.

Explore Free Courses

Suggested Blogs

Bagging vs Boosting in Machine Learning: Difference Between Bagging and Boosting
91134
Owing to the proliferation of Machine learning applications and an increase in computing power, data scientists have inherently implemented algorithms
Read More

by Pavan Vadapalli

25 May 2024

45+ Best Machine Learning Project Ideas For Beginners [2024]
330206
Summary: In this Article, you will learn Stock Prices Predictor Sports Predictor Develop A Sentiment Analyzer Enhance Healthcare Prepare ML Algorith
Read More

by Jaideep Khare

21 May 2024

Top 15 IoT Interview Questions & Answers 2024 – For Beginners & Experienced
64915
These days, the minute you indulge in any technology-oriented discussion, interview questions on cloud computing come up in some form or the other. Th
Read More

by Kechit Goyal

19 May 2024

40 Best IoT Project Ideas & Topics For Beginners 2024 [Latest]
766316
In this article, you will learn the 40Exciting IoT Project Ideas & Topics. Take a glimpse at the project ideas listed below. Best Simple IoT Proje
Read More

by Kechit Goyal

19 May 2024

Top 22 Artificial Intelligence Project Ideas & Topics for Beginners [2024]
416387
In this article, you will learn the 22 AI project ideas & Topics. Take a glimpse below. Best AI Project Ideas & Topics Predict Housing Price
Read More

by Pavan Vadapalli

18 May 2024

Image Segmentation Techniques [Step By Step Implementation]
64164
What do you see first when you look at your selfie? Your face, right? You can spot your face because your brain is capable of identifying your face an
Read More

by Pavan Vadapalli

16 May 2024

6 Types of Regression Models in Machine Learning You Should Know About
283786
Introduction Linear regression and logistic regression are two types of regression analysis techniques that are used to solve the regression problem
Read More

by Pavan Vadapalli

16 May 2024

How to Make a Chatbot in Python Step By Step [With Source Code]
31776
Creating a chatbot in Python is an essential skill for modern developers looking to enhance user interaction and automate responses within application
Read More

by Kechit Goyal

13 May 2024

Artificial Intelligence course fees
5865
Artificial intelligence (AI) was one of the most used words in 2023, which emphasizes how important and widespread this technology has become. If you
Read More

by venkatesh Rajanala

29 Feb 2024

Schedule 1:1 free counsellingTalk to Career Expert
icon
footer sticky close icon