AI Insights

Top 35 Deep Learning Projects Ideas in 2024 (For Beginners & Advanced)

December 17, 2021

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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 is deep learning?

What is deep learning?

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:

1. TensorFlow



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. 

2. PyTorch



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. 

3. Keras 



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

The diferrent between CPU vs GPUs

The diferrent between CPU vs GPUs

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. 

Top 35 Deep learning Project Ideas in 2024

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:

20 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 

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. 

8. Chatbot 


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. 

14. MNIST 

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. 

18. Music Genre Classification

Develop a deep learning model that can accurately classify music tracks into different genres. This project will involve preprocessing audio data, extracting relevant features, and training a neural network to classify the music genre. You can explore different architectures such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs) for this classification task.

19. Plant Disease Detection

Build a deep learning model that can accurately detect and classify plant diseases from images. This project will involve preprocessing the image data, training a convolutional neural network (CNN), and evaluating the model’s performance on a test dataset. You can explore transfer learning techniques using pre-trained models like ResNet or Inception for better accuracy.

Omdena has undertaken local chapter challenges related to plant disease detection. For example, the Ethiopia Local Chapter focuses on the use of computer vision to detect diseases in coffee plants. By leveraging computer vision techniques, this project aims to develop an accurate and efficient system for identifying diseases in coffee plants, which can greatly benefit coffee farmers in maintaining the health of their crops.

Another challenge from the São Paulo, Brazil Chapter revolves around the classification of plant diseases in Brazilian agriculture using computer vision and machine learning. With Brazil being a major agricultural country, this project aims to develop a robust system that can accurately classify various plant diseases, allowing farmers to take timely preventive measures and minimize crop losses.

Explore more AI project in Agriculture here!

20. Fake News Detection

Create a deep learning model that can distinguish between real and fake news articles. This project will involve preprocessing textual data, building a text classification model using recurrent neural networks (RNNs) or transformer-based architectures, and evaluating the model’s performance on a test dataset. You can also consider using techniques like attention mechanisms or BERT for better results.

15 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 

Damage Assessment in agriculture using deep learning

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 

Omdena will be developing an AI model for route optimization to optimize delivery planning for logistics companies. 

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. 

5. OpenCog

OpenCog is a project aimed at designing an open-source Artificial General Intelligence framework similar to what is used in Sophia, the AI robot. 

 6. DeepMimic

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 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. 

9. ChatGPT – OpenAI

ChatGPT – OpenAI

ChatGPT – OpenAI

GPT-3 (Generative Pretrained Transformer 3) is a state-of-the-art language model developed by OpenAI, which has been trained on a massive amount of text data from the internet.

ChatGPT is a variation of GPT-3 that has been fine-tuned for conversational response generation. It uses the GPT-3 model’s architecture and pre-trained weights, but has been trained further on conversational data to generate more human-like responses to text inputs.

10. DALL·E 2 – OpenAI

DALL·E 2 is an advanced AI model that generates 2D images from textual descriptions. It can generate diverse, high-resolution images of things like objects, animals, and scenes that don’t exist in the real world. It works by using a combination of deep learning algorithms and computer vision techniques to generate unique images based on a given text prompt. The model is trained on a massive dataset of images and textual descriptions, allowing it to generate high-quality images based on complex and abstract descriptions.

11. Real-time Emotion Recognition

Develop a deep learning model that can accurately recognize and classify human emotions in real-time from image or video data. This project could have applications in various fields, such as psychological research, market analysis, and human-computer interaction.

12. AI-Powered Medical Diagnosis

Build an AI system that can assist doctors in diagnosing medical conditions by analyzing medical images such as X-rays, CT scans, or MRIs. The deep learning model should be able to detect abnormalities, tumors, or other potential health issues with high accuracy.

13. Autonomous Driving with Reinforcement Learning

Create an autonomous driving system using reinforcement learning algorithms. Train a deep neural network to navigate a vehicle safely on various road conditions, obey traffic signs, and make optimal decisions in real-time.

14. Video Action Recognition

Develop a deep learning model to recognize and classify human actions from video sequences. The model should be able to identify activities such as walking, running, swimming, playing sports, etc., from video footage.

15. Generative Adversarial Networks for Art Generation

Use Generative Adversarial Networks (GANs) to generate realistic and creative artworks. Train a GAN model to learn the style and characteristics of famous artists and generate new artworks that mimic their unique styles.


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 OmdenaAcademy with real case studies by our passionate instructors.

Ready to test your skills?

If you’re interested in collaborating, apply to join an Omdena project at:

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