Neural networks aim to recognize underlying relationships in datasets through a process that mimics the functioning of the human brain. 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. Read on to familiarize yourself with some exciting applications!
Fundamentals of neural networks
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.
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
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.
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.
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.
4. Cryptographic applications using artificial neural networks
Cryptography is concerned with maintaining computational security and avoiding data leakages in electronic communications. 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
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
With deep learning, it is possible to make real music without knowing how to play any instruments. 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
Neural network implementations have the potential to introduce efficiency in medical diagnosis, and particularly in the field of cancer detection. Since cancer cells are different from healthy cells, it is possible to detect the ailment using histology images. 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
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
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.
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 also implement a neural network-based model to detect human activities – for example, 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.
The job market of the future is likely to prefer individuals with training in machine learning, along with adequate methodology skills. So, enhance your subject knowledge and practical capabilities with these neural network projects to gain a competitive advantage!
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.