Ever thought about how machines can compose music or identify faces in pictures? It’s all thanks to something called neural networks. They’re like super-smart brains that computers can use to learn and solve problems.
This guide allows you to dive into neural networks, even if you’re just starting. We’ll simplify the complicated parts into easy steps and teach you how to use them to create cool projects.
Let’s get started!
8 Skills That Neural Network Projects Can Help You Practice
If you want to get better at using neural networks (NN), practicing with real-world projects is key. Here are some skills you can work on while doing NN projects:
- Programming Languages: NN projects involve coding in Java, Python, and C++. Doing projects helps you get better at coding in these languages.
- Statistical Models: NN is a part of deep learning, which requires understanding statistics and statistical models. Working on NN projects helps you improve your statistical skills.
- Deep Learning Libraries: Libraries like TensorFlow are used to build large-scale neural networks. You’ll learn how to use these libraries effectively by working on projects.
- Data Analysis and Visualization: NN needs data to learn and make predictions. Projects involve cleaning data and analyzing patterns, improving your data analysis skills.
- Critical Thinking and Problem-Solving: NN projects often require troubleshooting and fine-tuning models. This helps you develop critical thinking and problem-solving skills.
- Mathematics: Mathematics is important in building neural networks. Skills like linear algebra and calculus are crucial; you’ll improve them while working on NN projects.
- Algorithms: NN consists of algorithms governing their functions. By working on projects, you’ll practice and learn these algorithms, improving your understanding of deep learning.
- Creativity and Experimentation: NN architecture and training techniques are always changing. Projects allow you to experiment with different techniques, fostering creativity and innovation.
By working on neural network projects, you can improve these skills and become better at using neural networks to solve real-world problems.
Press the button to get a file with information about project ideas involving neural networks. The file will be a PDF document that you can read at your own speed. You can refer to this PDF file whenever you need the information again.
Top 7+ Neural Network Project Ideas With Source Code 2024
Explore these seven Neural Network Project Ideas with accompanying Source Code. They’re perfect for trying out in 2024. Each idea comes with a clear explanation of what you’ll learn and what the project accomplishes.
1. Neural Network Project on Automatic Music Generation System
You can create real music even if you don’t know how to play instruments. People use this for fun or to make music professionally. You can use MIDI files to make these programs, and you can even use a special type of computer program called an LSTM model to create new and exciting songs.
What it does:
- Make Melodies: It creates melodies by determining what note should come next based on the notes before it.
- Set Rhythm: You can control how fast or slow the music goes and how long each note lasts, making it sound more natural.
- Change Style: You can teach the tool different music styles like classical, jazz, or pop, and then choose which style you want your music to be in.
- Choose Instruments: You can pick which instruments you want your music to play, but this might be tricky if you’re just starting.
What you’ll learn:
- Deep Learning Basics: You’ll understand how neural networks work, especially a type called Long Short-Term Memory (LSTM) networks, and how they’re used in making music.
- Programming in Python: You’ll get hands-on experience using Python tools like NumPy, Pandas, and TensorFlow.
- Preparing Data: You’ll learn how to prepare music files for the tool.
- Evaluating Music: You’ll discover ways to check if the music the tool makes sounds good and matches your desired style.
Automatic Music Generation System Github Link
2. Face detection system
A face detection system automatically uses deep learning to find and pinpoint human faces in pictures or videos. It works by using special kinds of computer programs called convolutional neural networks (CNNs).
These programs can quickly analyze images or videos to spot faces accurately and in real time. The system is good at getting things right while not needing too much computing power.
It uses advanced deep learning methods like YOLO (You Only Look Once) or SSD (Single Shot Multibox Detector).
What you’ll learn:
- How to spot faces in videos as they happen (you can skip this if you’re just starting out).
- Drawing boxes around faces you find in pictures or videos.
- Finding multiple faces in a single scene.
Tools you’ll use:
- OpenCV (also known as cv2): This is a free software library for computer vision tasks. It’s often used with other software that helps computers learn from data.
Skills you’ll develop:
- Getting images ready for the computer to understand, like making them smaller or changing their colors.
- Understanding a type of computer program called a Convolutional Neural Network, or CNN. These are often used to find faces (for example, ones called VGG-Face or MTCNN).
- Making more training data for your computer to learn from can help it work better (for example, by flipping or rotating images).
- Check how well your face-finding system is working.
CNN for face detection Github link.
Must Read: 7 Unique TensorFlow Project Ideas To Level Up Your Skills
3. Chatbot
Creating a chatbot with deep learning is an exciting project. Chatbots can work in different ways, but a smart one will use deep learning to understand the user is question and give the right answer.
Here’s a simple guide for beginners on making a chatbot using deep learning, TensorFlow, and an NMT sequence-to-sequence model.
Chatbot Github Link
What It Does:
- Understands what you’re asking or saying (like if you’re asking a question or wanting something).
- Figure out specific details in what you say (like what you’re asking about in a weather question).
- It talks back to you in a way that sounds normal and interesting.
How It’s Made:
- Uses a beginner-friendly tool called Keras (with TensorFlow) that has ready-made parts for making chatbots.
What You’ll Learn:
- How to make sense of human language (NLP).
- How to build and train models like Recurrent Neural Networks (RNNs) for chatbots.
- How to get text ready for training the model (like organizing conversations).
- How to measure how good the chatbot is (like how often it gets things right and how well it talks).
4. Driver Drowsiness Detection
The driver drowsiness detection project helps determine if someone driving is falling asleep. Creating a system for drivers can stop accidents before they occur.
What it does:
- Live Video Check: Keeps an eye on the video from a camera in the car.
- Spotting Faces and Eyes: Find where the driver’s face is and look for their eyes.
- Checking Eye Blinking: Learns to tell if the driver’s eyes are open or closed.
- Noticing Sleepiness: Watches how long the driver’s eyes are closed and how often they blink to see if they’re getting sleepy.
- Warning System: Makes a noise or vibration or sends a message if it thinks the driver might be nodding off.
How it works:
- OpenCV: A tool that helps find things in images, like faces and eyes.
- TensorFlow/Keras: Helps build and teach a computer to spot open and closed eyes.
Things you learn:
- Spotting Things in Images: Techniques for finding faces and eyes in pictures.
- Teaching Computers with CNNs: Learning to use a type of computer learning called CNNs to determine whether eyes are open or closed.
- Doing Things Fast: Figuring out how to watch videos and decide quickly.
Driver Drowsiness Detection Github Link
Must Read: Top 211+ Exciting Hackathon Project Ideas You Must Try
5. Human Activity Detection
The smartphone dataset has records of fitness activities and details of 30 people. This information was collected using a smartphone with special sensors.
This machine learning project wants to create a model that can accurately recognize people’s different fitness activities.
What we’ll do:
- We’ll teach a computer to recognize activities like walking, running, and jumping using data from sensors like those in a fitness tracker.
How we’ll do it:
- We’ll use a software called TensorFlow/Keras, which is good for beginners and has lots of helpful information available.
What you’ll learn:
- Cleaning sensor data: We’ll show you how to make it neat and tidy before the computer learns from it.
- Making a simple brain: You’ll understand how to make a basic computer brain called a neural network. We’ll probably use a type called LSTM for this project.
- Spotting activities in real-time: You’ll get to try out recognizing activities as they happen, using data from sensors in real time.
Human Activity Detection Github Link
6. Object Detection
An object detection system is different from facial recognition. While facial recognition focuses only on identifying faces, object detection identifies all kinds of objects in a picture.
This is useful for businesses, especially security companies, to spot potential threats. Even Tesla’s autopilot uses object detection to see objects on the road and avoid accidents.
You can use pre-made models like Resnet50, Yolo, and SSD to make an object detection system. These models are already built into Keras and Tensorflow, making it simple for you to begin.
What you can do:
- Identify common objects like cars, people, and animals in pictures and draw boxes around them.
- Detect objects in real-time using a webcam and draw boxes around them.
- Go beyond basic objects and classify specific categories like dog breeds or traffic signs.
- Train the model to detect something specific you’re interested in, like types of food or musical instruments.
Recommended Framework:
- TensorFlow/Keras is a popular choice for this project, with many helpful tutorials available.
What you’ll learn:
- Understand how Convolutional Neural Networks (CNNs) work for object detection.
- Fine-tune pre-existing models to recognize the objects you’re interested in.
- Prepare image datasets by labeling them with bounding boxes.
- Assess the accuracy of your object detection using metrics like mAP.
Object Detection Github Link
7. Recommendation systems
Recommender systems are like friendly guides on websites and apps that suggest things you might like.
Big companies such as Netflix, Spotify, LinkedIn, and Amazon use these systems to help users find things they might enjoy, like movies, music, job listings, or products to buy.
A while back, Netflix even had a big contest where they gave out a huge dataset of movies for people to play with.
People worldwide competed to see who could make the best predictions about which movies users would like. The winning team got a cool million bucks from Netflix!
Recommendation system Github Link
What you can do:
- Content-based Recommendations: This means suggesting things similar to what you’ve liked. For example, if you liked a certain movie, it might suggest others in the same genre.
- Collaborative Filtering: This suggests things based on what people with similar tastes to yours have liked. So if you like certain movies, it might suggest others that people with similar tastes also enjoy.
- Hybrid Recommendation System: This mixes the first two methods, improving the suggestions.
Tools you’ll use:
- TensorFlow/Keras: This is a toolkit that makes it easier to build recommendation systems. You can find more info here.
What you’ll learn:
- Deep Learning Architectures: Learning about different kinds of neural networks used in recommendation systems.
- Data Preprocessing: Getting data ready to train your model.
- Evaluation Metrics: Figuring out how well your recommendation system is working.
- Model Tuning: Make your recommendation system even better by tweaking some settings.
Must Read: Top 18 React Native Project Ideas for All Levels In 2024
8. Dog Breed Identification
We need you to create a special computer program that can tell apart different types of dogs just by looking at pictures of them. You can start by using a big collection of dog pictures from a website called Kaggle.
What you can do:
- Teach a computer to look at pictures of dogs and guess what kind of dog it is.
- Tell the difference between lots of different types of dogs.
- (Optional) Get a score that shows how sure the computer is about its guess.
The best way to do this:
- Use TensorFlow or Keras, popular tools for teaching computers to understand pictures.
What you’ll learn:
- How to use Convolutional Neural Networks (CNNs) to teach computers to recognize images.
- How to use pre-made models to make teaching computers faster.
- How to get pictures of dogs ready for teaching computers.
- (Optional) Tricks for making your training data bigger and more varied.
Dog Breed Identification Github Link
9. Fake News Detection
Mobile apps make staying updated with the latest news easy in today’s digital world. But can we always trust what we read on these apps?
Unfortunately, no. Let’s take WhatsApp as an example. During the COVID-19 pandemic, many false messages circulated on the platform, claiming cures or prevention methods.
Sadly, many believed and followed these messages, leading to harmful consequences. Companies like Facebook and Google use AI to find and delete fake news from their platforms to tackle this issue.
What you can do:
- Text Sorting: Make a computer learn to sort news stories into real or fake piles by reading them.
- Headline Check: Teach the computer to spot tricky or exaggerated words in headlines that usually come with fake news.
- Website Checking: Make the computer look at how trustworthy a website is to help it spot fake news better. (This one is a bit harder.)
- All-in-One Check: Get fancy by having the computer look at words, pictures or videos to find fake stuff. (This one’s the toughest!)
The best way to do this:
- TensorFlow/Keras: This is like a toolkit many people like because it’s easy to use when teaching a computer about words and stuff.
What you’ll learn:
- Getting Words Ready: You’ll learn how to clean up and get words ready for the computer to read.
- Language Tricks: Learn about different ways computers understand and play with words.
- Deep Learning: You’ll use special networks to understand the order of words, like in a story.
- Checking Your Work: Learn to see if the computer’s doing a good job at spotting fake news.
Fake News Detection Github LInk
Wrap Up
Neural networks can help solve all sorts of problems in really cool ways. By practicing, you can create projects that are both enjoyable and helpful.
This guide is just the start. Keep trying new things, play with ideas, and don’t worry about being imaginative! The future of neural networks looks promising, and you can contribute to it.
FAQs
What is CNN deep learning?
CNN stands for Convolutional Neural Network. It’s like a smart system that looks at pictures and figures out what’s in them by assigning importance to different parts of the image.
What should I know before learning neural networks?
Before diving into neural networks, it’s important to be comfortable with math, especially calculus, linear algebra, probability, and statistics. You should also be familiar with coding and have some understanding of machine learning algorithms like linear logistic regression.
What is Keras API?
Keras is a Python tool for deep learning. It works on top of TensorFlow, a machine-learning platform. Keras is designed to make it easy for people to quickly try out different ideas and experiments.
