Whether you are a beginner or have been in the field of deep learning for some time, you could always use some inspiration through deep learning project ideas. Deep learning is an evolving field. Seeing what others have done could inspire you to develop a solution to deal with a problem you are passionate about.
That said, let’s first look into the basics of deep learning.
What is Deep Learning?
What if machines could operate like the human brain? This is the fundamental idea behind deep learning.
But what exactly is deep learning?
Deep learning is a subsection of machine learning that uses artificial neural networks to emulate the cognitive abilities of the human brain. The goal of deep learning is to develop computer systems that can function independently without input from humans.
While the concept of deep learning has been around since the 1950s, it wasn’t until recently that its applications materialized.
What are the Benefits of Using It?
Learning how to use deep learning will be critical to solving some of the world’s problems through technology. We’ve already seen some of the benefits of using deep learning. They include:
- Deep learning algorithms can execute feature engineering independently, thus improving accuracy and efficiency.
- Deep learning produces the best results with unstructured data, by training deep learning algorithms to derive insights from different data formats.
- Deep learning algorithms are adept at detecting anomalies or inconsistencies that a human would otherwise miss. This level of accuracy is important in medical settings where early detection and diagnosis of medical conditions can save lives.
- Well-trained deep learning algorithms can perform thousands of repetitive tasks fast without fatigue or diminishing productivity.
To learn how to use deep learning, you must first identify the frameworks to use to develop a deep learning project capable of solving the problem you have in mind.
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How to Choose the Best Framework for Deep Learning Projects
You’ll find many frameworks for deep learning. Each of these takes time to learn, and some are more suited to certain projects. With so many choices, it may be hard to choose from the best frameworks for deep learning.
In this section, we will cover some of the most recommended frameworks for deep learning to help you choose one that suits your project needs.
Here’s a list of frameworks for deep learning to choose from:
TensorFlow is a deep learning framework developed at Google Brain. The open-source project can perform regression, classification, and neural networks. The framework is available for both CPUs and GPUs.
TensorFlow is ideal for both beginners and advanced deep learning specialists. The framework requires a good understanding of NumPy arrays and Python.
Like TensorFlow, PyTorch uses python. PyTorch is ideal for larger projects that require customization.
TensorFlow and PyTorch are the most popular and highly recommended frameworks for deep learning projects.
Keras framework is a neural network library designed on TensorFlow to make machine learning modeling easier. It can run on a CPU or GPU. Keras can be used with R, Theano, PlaidML, and Microsoft Cognitive Toolkit (CNTK). Keras is regarded as one of the best frameworks for deep learning projects for beginners.
These three are not the only deep learning frameworks available. Others include Sonnet, MXNet, Gluon, DL4J, ONNX, and Chainer.
To choose the right machine learning framework, you should think about several factors:
- Your project needs
- Parameter optimization
- Scaling, training, and deployment
Which Hardware Should You Use in Your Projects
Once you figure out the best framework for deep learning to use, the next step is determining the hardware to choose. When thinking about hardware, the question is whether to use a CPU or GPUs or both in a machine.
Since deep learning projects require a lot of computational power, you need to choose the hardware that supports your project needs best. Let’s briefly compare CPUs and GPUs for deep learning projects.
Before we do that, you should understand that between CPUs and GPUs, none is better than the other. Each hardware has its own distinct properties that make it ideal for certain projects over others.
|Can run any type of calculation||Ideal for projects that require parallel computing|
|Have sequential operation capability making them ideal for linear and complex calculations e.g. Recurrent neural networks||Ideal for projects that involve large-scale problems or data|
|Ideal for memory-intensive training and inference|
Both CPUs and GPUs have their place in deep learning, and the choice boils down to factors such as price, energy consumption, and speed.
25 Deep learning Project Ideas in 2022 for Final Year
Once you’ve figured out whether you want to have a CPU or GPUs or both in a machine the next step is to draw inspiration from other deep learning specialists. Here is a list of deep learning project ideas:
17 Best Deep Learning Projects Ideas for Beginners
1. Visual Tracking System
A visual tracking system uses a camera to track the movement of a moving object over a given time frame. Visual tracking systems are popular in traffic control, security, surveillance, and augmented reality.
2. Face Detection System
In this project, the goal is to create a project focused on tracking and visualizing human faces within digital objects.
3. Driver Drowsiness Detection
Driver drowsiness detection systems are designed to identify signs of drowsiness and alert the driver. You will use Python, Open CV, and Keras for the project.
4. Image Caption Generator
The image caption generator uses Convolutional Neural Networks and LTSM to generate captions for an image. The goal of the project is to use computers to analyze the context of an image and generate relevant captions.
5. Colorizing Old B&W Images
The focus of this project is to simplify the automated colorization of B&W photos. You will use Python and OpenCV DNN architecture to color these photos.
6. Image Classification with CIFAR Dataset
Image classification is an ideal beginner project. In this project, the goal is to build an image classification system based on the CIFAR-10 dataset which consists of more than 60,000 images.
7. Dogs Breed Identification
This project requires you to develop a deep learning model for distinguishing between different dog breeds from an image. You can use Kaggle’s dog breed dataset to begin the project.
Chatbot is a simple project that requires you to compile queries and their corresponding responses for the chatbot and then test the chatbot. This project uses Python. You can learn more in this article on How to Build a Chatbot for AI Driving Assistant
9. Dogs and Cats
The dogs and cats project is a project that requires you to train your model with images of cats and dogs to develop a classification system that can distinguish between these images.
10. Object Detection
The goal of this project is to identify a specified object and mark the specified positions in an object.
11. Real-time Image Animation
Real-time image animation is an open-source project that requires the use of OpenCV to animate a still image.
12. Kaggle Titanic Prediction
This deep learning project consists of a dataset of passengers who traveled on the Titanic. The goal is to predict the passengers that survived the Titanic.
13. House Price Prediction
This project uses house price data using the Kaggle House Price Prediction Dataset to determine what a particular house costs based on price, location, etc.
The MNIST dataset consists of images of handwritten numbers 0-9. The goal is to develop a classification system that can recognize these handwritten numbers.
15. Predict Next Sequence
The goal of this project is to develop and train a model to predict the next digit in a sequence.
16. Variation Autoencoders
Variation Autoencoders can generate fresh data similar to the training data. The MNIST dataset is a good place to start generating numbers.
17. Language Translator
The goal of this project is to create a translation app that can translate from one language to another.
8 Advanced Deep Learning Project Ideas
1. Bringing Old Photos Back to Life – Microsoft
The project is designed to restore old degraded photos through scratch detection face enhancements and other deep learning techniques.
2. Damage Assessment – Omdena
Omdena, in partnership with OKO, completed a deep learning project using satellite images to detect and assess the damage armyworms caused in farming.
3. Route Optimization for Logistics Industry – Omdena
You can find the full case study in the article Delivery Route Optimization in LATAM using AI Planning
4. Detectron – Facebook
Detectron is a deep learning project based on the Caffe2 deep learning framework. It offers a high-quality and performance codebase for detection research with over 50 pre-trained models.
OpenCog is a project aimed at designing an open-source Artificial General Intelligence framework similar to what is used in Sophia, the AI robot.
DeepMimic is a good project idea for the advanced level. The project is a neural network trained to simulate an object using motion capture data.
7. Google Brain
Google began the Google Brain research project in 2011. Google Brain has the largest neural networks for machine learning with 16000 connected computer processors.
8. Lung Cancer Detection – 12 Sigma
12 Sigma developed the lung detection AI algorithm designed to detect lung cancer in its early stages, faster than traditional methods can.
Whether you are a beginner or at an advanced level, these deep learning project ideas could inspire you to develop your own projects.
Tell us what you think about these ideas in the comments. Feel free to share other ideas you may have with our community.
Inspired? Start your own deep learning projects today or take courses at Omdenaschool with real case studies by our passionate instructors.
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