Blog_Banner_Asset
    Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconArtificial Intelligencebreadcumb forward arrow icon13 Interesting Neural Network Project Ideas & Topics for Beginners [2024]

13 Interesting Neural Network Project Ideas & Topics for Beginners [2024]

Last updated:
7th Sep, 2023
Views
Read Time
13 Mins
share image icon
In this article
Chevron in toc
View All
13 Interesting Neural Network Project Ideas & Topics for Beginners [2024]

The topic of neural networks has captivated the world of artificial intelligence and machine learning with its ability to mimic the human brain’s learning process. Neural networks aim to recognize underlying relationships in datasets through a process that mimics the functioning of the human brain.

Modern developments in image recognition, autonomous vehicles, and many other fields are based on application of neural networks. These adaptable frameworks have revolutionized the way we use technology. Such systems can learn to perform tasks without being programmed with precise rules. You can implement different neural network projects to understand all about network architectures and how they work.

In this article, we will delve into the fundamental principles of neural networks, and witness their capabilities through real-world Advanced deep learning projects ideas and understand what is neural network in AI? Read on to familiarize yourself with some exciting applications!

Fundamentals of neural networks

The fundamentals of neural networks algorithms lie in their inspiration from the human brain’s neural networks and their ability to learn from data. These networks are useful tools for a variety of machine-learning applications since they are built to analyze data and identify patterns.

Ads of upGrad blog

Before we begin with our list of neural network project ideas, let us first revise the basics.

  • A neural network is a series of algorithms that process complex data
  • It can adapt to changing input.
  • It can generate the best possible results without requiring you to redesign the output criteria.
  • Computer scientists use neural networks to recognize patterns and solve diverse problems.
  • It is an example of machine learning.
  • The phrase “deep learning” is used for complex neural networks.

Understanding these fundamentals is essential for building, training, and deploying effective application neural network models.

Today, neural networks are applied to a wide range of business functions, such as customer research, sales forecasting, data validation, risk management, etc. And adopting a hands-on training approach brings many advantages if you want to pursue a career in deep learning. So, let us dive into the topics one by one. Learn more about the applications of neural networks.

Neural Network Projects

Here are few examples of Neural network projects with source code

1. Autoencoders based on neural networks

Autoencoders are the simplest of deep learning architectures. They are a specific type of feedforward neural networks where the input is first compressed into a lower-dimensional code. Then, the output is reconstructed from the compact code representation or summary. Therefore, autoencoders have three components built inside them – encoder, code, and decoder. In the next section, we have summarized how the architecture works.

  • The input passes through the encoder to produce the code.
  • The decoder (mirror image of the encoder’s structure) processes the output using the code.
  • An output is generated, which is identical to the input. 

From the above steps, you will observe that an autoencoder is a dimensionality reduction or compression algorithm. To begin the development process, you will need an encoding method, a decoding method, and a loss function. Binary cross-entropy and mean squared error are the two top choices for the loss function. And to train the autoencoders, you can follow the same procedure as artificial neural networks via back-propagation. Now, let us discuss the applications of these networks. 

You can create a handwriting recognition tool using the MNIST dataset as input. MNIST is a manageable, beginner-friendly data source that can be used to generate images of handwritten numbers. Since these images are noisy, they need a noise removal filter to classify and read the digits properly. And autoencoders can learn this noise removal feature for a particular dataset. You can try this project yourself by downloading freely available code from online repositories. 

Must Read: Free nlp online course!

2. Convolutional neural network model 

Convolutional neural networks or CNNs are typically applied to analyze visual imagery. This architecture can be used for different purposes, such as for image processing in self-driving cars.

Autonomous driving applications use this model to interface with the vehicle where CNNs receive image feedback and pass it along to a series of output decisions (turn right/left, stop/drive, etc.) Then, Reinforcement Learning algorithms process these decisions for driving. Here is how you can start building a full-fledged application on your own:

  • Take a tutorial on MNIST or CIFAR-10.
  • Get acquainted with binary image classification models.
  • Plug and play with the open code in your Jupyter notebook.

With this approach, you can learn how to import custom datasets and experiment with the implementation to achieve the desired performance. You can try increasing the number of epochs, toying with images, adding more layers, etc. Additionally, you can dive into some object detection algorithms like SSD, YOLO, Fast R-CNN, etc. Facial recognition in the iPhone’s FaceID feature is one of the most common examples of this model. 

Once you have brushed up your concepts, try your hand at constructing a traffic sign classification system for a self-driving car using CNN and the Keras library. You can explore the GTSRB dataset for this project. Learn more about convolutional neural networks.

Learn Machine Learning courses from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career.

3. Recurrent neural network model

Unlike feedforward nets, recurrent neural networks or RNNs can deal with sequences of variable lengths. Sequence models like RNN have several applications, ranging from chatbots, text mining, video processing, to price predictions. 

If you are just getting started, you should first acquire a foundational understanding of the LSTN gate with a char-level RNN. For example, you can attempt loading stock price datasets. You can train RNNs to predict what comes next by processing real data sequences one by one. We have explained this process below:

  • Assume that the predictions are probabilistic.
  • Sampling iterations take place in the network’s output distribution.
  • The sample is fed as input in the next step.
  • The trained network generates novel sequences.

With this, we have covered the main types of neural networks and their applications. Let us now look at some more specific neural network project ideas

Best Machine Learning and AI Courses Online

4. Cryptographic applications using artificial neural networks

Cryptography is concerned with maintaining computational security and avoiding data leakages in electronic communications.

 Artificial neural network-based cryptographic applications represent an intriguing merging of cutting-edge technologies to address pressing problems with security, privacy, and data protection. Cryptographic applications utilising neural networks have the potential to change the face of modern cybersecurity and data protection as research in this field advances.

You can implement a project in this field by using different neural network architectures and training algorithms. 

Suppose the objective of your study is to investigate the use of artificial neural networks in cryptography. For the implementation, you can use a simple recurrent structure like the Jordan network, trained by the back-propagation algorithm. You will get a finite state sequential machine, which will be used for the encryption and decryption processes. Additionally, chaotic neural nets can form an integral part of the cryptographic algorithm in such systems.  

5. Credit scoring system

Loan defaulters can stimulate enormous losses for banks and financial institutions. Therefore, they have to dedicate significant resources for assessing credit risks and classifying applications. In such a scenario, neural networks can provide an excellent alternative to traditional statistical models.

They offer a better predictive ability and more accurate classification outcomes than techniques like logistic regression and discriminant analysis. So, consider taking up a project to prove the same. You can design a credit scoring system based on artificial neural networks, and a draw a conclusion for your study from the following steps:

  • Extract a real-world credit card data set for analysis.
  • Determine the structure of neural networks for use, such as mixture-of-experts or the radial basis function.
  • Specify weights to minimize the total errors.
  • Explain your optimization technique or theory.
  • Compare your proposed decision-support system with other credit scoring applications.

6. Web-based training environment

If you want to learn how to create an advanced web education system using modern internet and development technologies, refer to the project called Socratenon. It will give you a peek into how web-based training can go beyond traditional solutions like virtual textbooks. The project’s package has been finalized, and its techniques have been tested for their superiority over other solutions available from open literature. 

Socrantenon demonstrates how existing learning environments can be improved using sophisticated tools, such as:

  • User modeling to personalize content for users
  • Intelligent agents to provide better assistance and search
  • An intelligent back-end using neural networks and case-based reasoning

In-demand Machine Learning Skills

7. Vehicle security system using facial recognition

For this project, you can refer to SmartEye, a solution developed by Alfred Ritikos at Universiti Teknologi Malaysia. It covers several techniques, from facial recognition to optics and intelligent software development. 

Over the years, security systems have come to benefit from many innovative products that facilitate identification, verification, and authentication of individuals. And SmartEye tries to conceptualize these processes by simulation. Also, it experiments with the existing facial recognition technologies by combining multilevel wavelet decomposition and neural networks.

8. Automatic music generation

An interesting and cutting-edge use of artificial intelligence that has the potential to transform the music industry and creative processes is automatic music generation utilising neural networks.With deep learning, it is possible to make real music without knowing how to play any instruments.  You can train machines to write music, harmonise tunes, and create new musical compositions . You can create an automatic music generator using MIDI file data and building an LSTM model to generate new compositions.

OpenAI’s MuseNet serves as the appropriate example for this type of project. MuseNet is a deep neural network programmed to learn from discovered patterns of harmony, style, and rhythm and predict the next tokens to generate musical compositions. It can produce four-minute-long pieces with ten different instruments and combine forms like country music and rock music. 

Learn more: Introduction to Deep Learning & Neural Networks

9. Application for cancer detection

Another ground breaking idea for Deep learning projects with source code is in area of medicine for the diagnosis of cancer, which holds great promise for breakthroughs in early identification and better patient outcomes. Neural network implementations have the potential to introduce efficiency in medical diagnosis, and particularly in the field of cancer detection. There are numerous benefits to employing neural networks to detect cancer:

  • Early Detection
  • Speed and Efficiency
  • Accuracy
  • Scalability

Since cancer cells are different from healthy cells, it is possible to detect the ailment using histology images. In order to learn the intricate patterns and traits associated with cancer, neural networks can be trained on enormous databases of medical pictures. This allows them to detect malignant spots more effectively and reliably. For example, a multi-tiered neural network architecture allows you to classify breast tissue into malignant and benign. You can practice building this breast cancer classifier using an IDC dataset from Kaggle, which is available in the public domain.

10. Text summarizer

By leveraging the capabilities of neural networks, this technology can efficiently extract key information and essential details from documents, articles, or other textual sources, compressing the content while preserving the main ideas and context.

Automatic text summarization involves condensing a piece of text into a shorter version. For this project, you will apply deep neural networks using natural language processing. The manual process of writing summaries is both laborious and time expensive. So, automatic text summarizers have gained immense importance in the area of academic research.

11. Intelligent chatbot

A fascinating use of artificial intelligence and natural language processing (NLP) is developing intelligent chatbots using neural networks. Chatbots can be created to comprehend natural language questions and answer in a human-like manner by utilising the power of neural networks, providing individualised and contextually appropriate interactions.

Modern businesses are using chatbots to take care of routine requests and enhance customer service. Some of these bots can also identify the context of the queries and then respond with relevant answers. So, there are several ways to implement a chatbot system.

You can implement a project on retrieval-based chatbots using NLTK and Keras. Or you can go for generative models that are based on deep neural networks and do not require predefined responses. 

Read: How to make chatbot in Python?

12. Human pose estimation project

This project will encompass detecting the human body in an image and then estimating its key points such as eyes, head, neck, knees, elbows, etc. It is the same technology Snapchat and Instagram use to fix face filters on a person. You can use the MPII Human Pose dataset to create your version. 

13. Human activity recognition project

You can explore various Neural Network Project ideas, including implementing a neural network-based model for detecting human activities, such as sitting on a chair, falling, picking something up, opening or closing a door, etc. This is a video classification project, which will include combining a series of images and classifying the action. You can use a labeled video clips database, such as 20BN-something-something. 

Neural networks and deep learning have brought significant transformations to the world of artificial intelligence. Today, these methods have penetrated a wide range of industries, from medicine and biomedical systems to banking and finance to marketing and retail.

Popular AI and ML Blogs & Free Courses

 

Conclusion

The journey of exploring neural networks has been one of the most exhilarating phases of my career. Diving into this complex yet fascinating world of artificial intelligence, I’ve had the opportunity to work on various projects, each teaching me something unique about how machines can mimic human brain functions. From these experiences, I’ve compiled a list of Neural Network Project Ideas for Beginners, aimed at helping those just starting out in this field. When I began, the challenge wasn’t just understanding the intricate details of neural networks but also finding practical projects to apply this knowledge. This article is crafted from my firsthand experiences, offering a blend of practical project ideas and topics that are perfectly suited for beginners. Whether you’re a student, a budding professional, or simply an AI enthusiast, these neural network project ideas are tailored to kickstart your journey into the world of artificial intelligence. 

If you’re interested to gain Machine Learning certification, check out IIIT-B & upGrad’s Executive PG Programme 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.

Ads of upGrad blog

Refer to your Network!

If you know someone, who would benefit from our specially curated programs? Kindly fill in this form to register their interest. We would assist them to upskill with the right program, and get them a highest possible pre-applied fee-waiver up to 70,000/-

You earn referral incentives worth up to ₹80,000 for each friend that signs up for a paid programme! Read more about our referral incentives here.

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)

1What are Artificial Intelligence projects?

Artificial Intelligence (AI) projects enable machines to perform tasks that would otherwise require human intelligence. Learning, thinking, problem-solving, and perception are all goals of these intelligent creatures. Many theories, methodologies, and technologies are used in AI. Machine learning, neural networks, expert systems, cognitive technologies, human computer interaction, and natural language are just a few of the subfields. Graphics rendering unit, Iot, Complex algorithms, and API are some of the other AI-supporting technologies.

2What are the 4 types of AI?

AI can be divided into four categories. Reactive machines are AI systems that do not rely on prior experience to complete a task. They have no memory and respond based on what they see. IBM's chess-playing supercomputers, Deep Blue, are an example. In order to act in current situations, people with limited memory rely on their past experiences. Autonomous vehicles are an example of limited memory. Theory of mind is a form of artificial intelligence system that allows machines to make decisions. None of them are as capable of making decisions as humans are. It is, nonetheless, making substantial progress. A self-aware AI system is one that is aware of its own existence. These systems should be self-aware, aware of their own condition, and able to predict the feelings of others.

3How does unlocking a phone using faceID work?

Face biometrics are used to unlock a phone in an artificial intelligence project. The AI application can extract image attributes using deep learning. Convolution neural networks and Deep autoencoders networks are the two primary types of neural networks used. It's also a four-step procedure. Detection and face recognition, face alignment, face extraction, and face recognition are the four methods.

Explore Free Courses

Suggested Blogs

Top 5 Natural Language Processing (NLP) Projects & Topics For Beginners [2024]
109145
What are Natural Language Processing Projects? NLP project ideas advanced encompass various applications and research areas that leverage computation
Read More

by Pavan Vadapalli

30 May 2024

Top 8 Exciting AWS Projects & Ideas For Beginners [2024]
98952
AWS Projects & Topics Looking for AWS project ideas? Then you’ve come to the right place because, in this article, we’ve shared multiple AWS proj
Read More

by Pavan Vadapalli

30 May 2024

Bagging vs Boosting in Machine Learning: Difference Between Bagging and Boosting
91335
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]
330930
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 9 Python Libraries for Machine Learning in 2024
76183
Machine learning is the most algorithm-intense field in computer science. Gone are those days when people had to code all algorithms for machine learn
Read More

by upGrad

19 May 2024

Top 15 IoT Interview Questions & Answers 2024 – For Beginners & Experienced
65097
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]
768463
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]
420353
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]
64456
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

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