Deep learning is a sub-field of machine learning — which, in turn, is a sub-field of AI — that deals with training models and artificial neural networks (ANNs) capable of replicating the working of a human brain.
Sounds like science fiction? Right! But it’s getting closer to reality with every passing day.
Deep learning is, right now, an ambitious field of research that has shown promising applications for transforming the world.
Examples of deep learning include Google’s DeepDream and self-driving cars. As such, it is becoming a lucrative field to learn and earn in the 21st century.
One way to effectively learn — or enhance your skills in — deep learning is with hands-on projects.
So, here we are presenting you with our pick of the ten best deep learning projects.
We have included a wide range of deep learning project ideas, from easy to advanced. All of these are deep learning projects with source code included.
Note: Want to know the differences between machine learning and deep learning? Check out this detailed machine learning vs. deep learning comparison!
10 Best Deep Learning Projects to Try Out
Project 1 - Face Detection and Recognition System
Expected Time to Complete - 2 to 4 hours
Level - Intermediate
Objective(s)
- To develop a face detection system using Python and OpenCV.
This is an excellent deep learning project idea to start your journey in the field of deep learning. The project aims to develop a face detection and recognition system using the Python programming language. So, it is recommended that you have basic knowledge of Python and the OpenCV library to accomplish this project easily.
If you are wondering why you should consider working on this project, you only need to know that facial recognition technology is one of the prime applications of deep learning. So, if you want to become a DL engineer, this project will help you make real progress.
The project has two objectives:
- To detect a human face in an image
- To recognize the face by matching it with the face data already available in a database
First, you will need to develop an algorithm that can scan an image and detect any human face present within the image. The portion of the image that contains the human face needs to be cropped so that it can be processed further.
In the second phase, your algorithm will pick the cropped image, extract the face features, and compare the output with the face data stored in the database. If the face data gets matched with existing face data, you can display the person’s name identified in the image.
Reference Free Projects on GitHub:
- Face-Detection-And-Recognition-Based-Attendance-System (Also uses Java.)
- Smart Store System (Also uses Batchfile.)
- Python--Face-Recognition-Based-Attendance-System
- faceRecognition
Project 2 - Neural Network Development
Expected Time to Complete - 1 to 2 hours
Level - Beginner
Objective(s)
- To build a simple neural network to understand how neural networks work.
Working on a neural network project is a great idea to get familiar with how deep learning works in real-world applications. As this project is specifically intended for beginners, your objective is limited to understanding the principles of neural networks.
To easily comprehend the concept of neural networks, you will need to develop a simple neural network within this project. There are plenty of Python libraries for neural network development that you can use to accomplish this project, such as Keras, TensorFlow, NumPy, and NeuroLab.
One easy way to proceed with this project is to define a Keras model, train it, and evaluate its performance. It will be quite straightforward for you to develop a neural network in Keras and understand how neural networks can be created.
Also, you can take a step further by using the neural network you developed within this project to make predictions on new datasets.
You must pay special attention while working on this project as you will be able to enhance both your skills and knowledge regarding neural networks and deep learning with it.
Reference Free Projects on GitHub:
- ShrinkBench (Uses Jupyter Notebook and Python.)
- FuzzyClassificator (Uses Python.)
- FPGA_Neural-Network (Uses VHDL and more.)
- Learn-Keras-for-Deep-Neural-Networks (Uses Jupyter Notebook.)
Project 3 - Image Classification Program with CIFAR-10 Dataset
Expected Time to Complete - 2 to 3 hours
Level - Beginner
Objective(s)
- To develop an image classification system that can analyze the input image and define the class to which it belongs.
Image classification is another popular application of deep learning that is highly popular among deep learning engineers. If you are interested in getting familiar with how deep learning is revolutionizing how images are being identified and classified automatically, this is one of the best deep learning projects for beginners to try out.
This project aims to create an image classification program that you need to train using the popular CIFAR-10 dataset. The dataset contains 6,000 images that belong to 10 different classes.
Moreover, the dataset also has 50,000 training images along with 10,000 test images.
You may be interested in knowing that the CIFAR-10 dataset is among the most popular libraries of images used for training computer vision algorithms. Using this dataset will allow you to train your image classification program to classify images with high accuracy.
This deep learning project will expose various deep learning topics and make it possible for you to understand them so that you can easily apply them in your future projects.
Reference Free Projects on GitHub:
- ImageClassificationWithCifar10 (Uses Python.)
- Image classifier with Keras and CIFAR-10 dataset (Uses Python.)
Recommended Course
Deep Learning A-Z™: Hands-On Artificial Neural Networks
Project 4 - Traffic Sign Recognition and Classification
Expected Time to Complete - 3 to 5 hours
Level - Intermediate
Objective(s)
- To develop an algorithm that can recognize the traffic signs from an image and classify them.
Here’s another amazing project for you to develop if you already have some experience working with deep learning. This project aims to devise an algorithm capable of analyzing images and searching for traffic signs within them. Moreover, the algorithm needs to be capable of classifying the traffic sign.
Just like the previous project, this project is also an image classification project based on deep learning. The advantage of working on this project is that you will strengthen your understanding of the convolution neural network (CNN) algorithm.
CNN is considered a highly efficient neural network architecture used to analyze images. Having experience working with CNN will open opportunities to work with deep learning projects in various industries, including gaming, automotive, and e-commerce.
For training the traffic sign recognition model, you can use the GTSRB dataset, with GTSRB being the acronym for the German Traffic Sign Recognition Benchmark. This dataset comprises nearly 39,000 traffic sign images that are classified into 43 classes.
Reference Free Projects on GitHub:
- Traffic Sign Classification using Deep Learning (Uses HTML and Jupyter Notebook.)
- Traffic-Sign-Recognition--HSV-SVM--BelgiumTSC (Uses Jupyter Notebook and Python.)
- Street-Signs-and-boards-classification-using-DCNN (Uses Jupyter Notebook and Python.)
- Traffic-sign-recognition-and-classification (Uses Python.)
Project 5 - Object and Motion Detection System
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Expected Time to Complete - 6 to 9 hours
Level - Advanced
Objective(s)
- To build a deep learning model that can detect objects and track their motion.
Object recognition and motion detection are primarily used for accomplishing various real-world tasks, such as video surveillance and activity recognition.
Building a system that can detect objects as well as track their motion is a challenging task. So, if you are new to deep learning, it’s recommended that you understand all the fundamentals associated with object detection before proceeding with this project.
While working on this project, you need to accomplish two major objectives:
- Develop an algorithm for recognizing the object within the video
- Modify the algorithm to allow tracking of the object’s motion
Developing this project with Python would be a good idea. There are plenty of Python libraries that will make it possible to create a robust and reliable object detection and motion tracking model.
If you are not comfortable working with Python, you can also develop this project with MATLAB and Simulink.
Reference Free Projects on GitHub:
- Botupdate (Uses Common Lisp.)
- autonomous_mobile_robot (Uses Jupyter Notebook, Python, et cetera.)
- opencv
- Motion-Detection-and-Tracking-For-Moving-Object-Background-Subtraction (Uses C++.)
Project 6 - Chatbot
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Expected Time to Complete - 3 to 5 hours
Level - Intermediate
Objective(s)
- To develop a chatbot that can engage in productive conversation with humans.
Chatbots have become quite popular among businesses thanks to their ability to automate various processes under customer support. Also, chatbots are one of the finest examples of the revolution brought by artificial intelligence and deep learning.
Under this project, you will be developing a deep learning-based chatbot capable of simulating human conversations in the form of text messages. You need to train and configure the chatbot in a way that can give proper responses to the users.
To make the conversations more realistic, train the bot to provide the intended reply to the user when a certain word or phrase is detected during the conversation.
For developing a chatbot, you will need to get familiar with numerous AI and deep learning concepts, like natural language processing (NLP), convolution neural network (CNN), multilayer perceptron (MLP), and retrieval-based neural network.
It is important to train the chatbot to make sure that it can simulate a human-like conversation with users in real-time. If you plan to build the chatbot with Python, consider using NLTK (Natural Language Toolkit) and TensorFlow platforms.
Reference Free Projects on GitHub:
- DeepPavlov (Uses HTML, Jupyter Notebook, and Python.)
- DeepQA (Uses CSS, HTML, JS, Python, and Shell.)
- Neural Network Dialog System Papers
Project 7 - Driver Drowsiness Detection System
Expected Time to Complete - 6 to 8 hours
Level - Advanced
Objective(s)
- To develop an intelligent system that can detect if the driver falls asleep and raises the alarm.
This is yet another great DL project for you to put your knowledge about deep learning to the test and enhance your skills.
The drowsiness detection system you need to build within this project should monitor a person and look for signs that can help assess if the person is feeling sleepy. The system will make it possible for drivers to avoid a mishap that can be caused due to drowsiness.
One of the effective ways to make this system work is by writing an algorithm that can actively check whether the driver’s eyes are open or closed. As soon as the system detects the closed eyes of the driver, it needs to alert the driver by raising the alarm. It would be good to play a sound that the driver can easily acknowledge and become aware of the situation.
To develop the drowsiness detection system, you can make use of Python, OpenCV, and Keras. You need to enable the system to take video as input from a webcam directed towards the driver’s face. The system should detect the driver’s face and monitor the eyes to classify them as open or closed.
Reference Free Projects on GitHub:
- driver-fatigue-detection-system (Uses Python.)
- Fatigue(Drowsiness) Detection using OpenCV (Uses Python.)
- Drowsiness-Detection-Using-Facial-Images (Uses Python.)
- Driver-Sleep-Detector (Uses Python.)
Project 8 - Lung Cancer Detection Using CNN
Expected Time to Complete - 5 to 7 hours
Level - Advanced
Objective(s)
- To develop a program that can assist with diagnosing lung cancer.
Deep learning plays a pivotal role in advancing the healthcare industry, particularly when it comes to diagnosing life-threatening diseases like cancer. In this project, you would aim to develop a deep learning model that can use certain parameters to detect the signs of lung cancer in human lungs.
Detecting lung cancer manually by observing the images generated by a CT scan is not effective. So, the motive here is to make the deep learning model analyze the CT scan images and identify cancerous lung nodules. To train the model, you can use the LIDC-IDRI dataset containing several CT scan images of lungs from 1,010 cases.
While working on the DL project, one should understand various deep learning algorithms and methods. Convolutional neural networks (CNNs), stacked autoencoder, and data augmentation are some of them.
The lung cancer detection program you will develop should analyze the CT scan images provided as input and highlight the regions containing cancerous lung nodules.
Reference Free Projects on GitHub:
- LungCancerProject (Uses Jupyter Notebook, Makefile, and Python.)
- Lung-Cancer-Detection-CNN-and-GAN (Uses Python.)
- Lung-Cancer-Detection-using-3D-CNN (Uses Jupyter Notebook.)
- Lung-Cancer-Detection-using-CNN (Uses Jupyter Notebook.)
Project 9 - Human Face Generator
Expected Time to Complete - 8 to 10 hours
Level - Advanced
Objective(s)
- To create a DCGAN-based algorithm capable of generating human faces.
Would you like to work on a deep learning project that is challenging enough to advance your skills to the next level? If yes, you surely want to work on this deep learning project to develop a system that can generate human faces.
Interestingly, the face generator you will develop within this project will generate human faces that don't exist. The algorithm will generate fresh human faces that are not inspired by any real human face.
To develop the face generator, you will have to work with DCGAN, which stands for Deep Convolutional Generative Adversarial Networks.
Two main models that will be powering your face generator are generator and discriminator. The role of the generator would be to get some random noise at its input and produce a shape that is roughly equivalent to a human face. The discriminator’s role would be to assess the generated shape and decide how real it looks. You need to train both models to improve their ability to generate fake human faces effectively.
Reference Free Projects on GitHub:
- face-generator (Uses Lua and Python.)
- Face-Generator (Uses HTML, Jupyter Notebook, and Python.)
- Face-Generator (Uses Jupyter Notebook.)
- Human-Face-Generator (Uses HTML, Jupyter Notebook, and Python.)
Project 10 - Language Translation System
Expected Time to Complete - 8 to 10 hours
Level - Advanced
Objective(s)
- To build a language translation system that can translate text from English to German.
Translation applications have existed for quite a while. However, the language translation systems developed in the early years could not perform the translation with high accuracy.
With AI, machine learning, and deep learning becoming the go-to options for developing intelligent technologies, the language translation systems of today have evolved drastically. The modern language translation systems powered by deep learning are known to be highly responsive and accurate.
In this project, you will aim to develop a language translation system capable of translating an input text in English to the German language (or you can decide any other language) and vice-versa.
To create the language translation system, you can use the recurrent neural network (RNN), which is highly popular for working with natural language processing (NLP). You will need to build, train, test, and iterate the model to get the desired output from your language translation system.
You can use Keras and TensorFlow if you develop this project in Python.
Reference Free Projects on GitHub:
- question-answering (Uses Jupyter Notebook.)
- Dense-Captioning (Uses Python.)
How is Deep Learning Changing the World?
Artificial intelligence achieved with deep learning has made promising advancements that most have never thought of a few years ago. By looking at the projects mentioned above, you will get a good idea of the revolutionary technologies built using deep learning.
Deep learning today is making it possible for cars to drive on their own. Virtual assistants like Siri and Alexa are becoming more intelligent and helpful due to deep learning. It also makes face detection powerful and accurate. There are endless examples of how deep learning is changing the world.
Today, almost all the leaders in the tech industry are putting their best efforts into harnessing the power of deep learning to develop intelligent AI-based systems. It’s quite amazing that deep learning has been revolutionizing various industries, including healthcare, hospitality, manufacturing, cybersecurity, and energy.
Conclusion
Hopefully, these deep learning projects will help you fulfill your ambitions, whether to get a job in the lucrative field of deep learning (or machine learning or AI) or submit a DL project for your college/university major or for any other intent. Practice is the key.
If you want to study deep learning with the best learning material available over the web — both free and paid — then check out these best deep learning tutorials. Good luck!
