8 Exciting Deep Learning Project Ideas

Exploring AI: Diverse Deep Learning Project Ideas for Data Enthusiasts

AI is very trending these days, especially Generative AI tools and Large Language Models like ChatGPT, Bard, mid-journey, and more. The foundation of many of these tools lies in Deep learning techniques.

To learn deep learning, you should do a lot of projects. Of course, if you have prior knowledge, you possibly did handwritten digit recognition or iris classification projects, but by doing exciting deep learning projects, not only you will hone your skills, you will have fun too!

In this article, we will go through different deep-learning project ideas to test your skills, and if you are a total beginner, it will be a great starting point. But first, let’s start with fundamentals.

What is Deep Learning?

Deep Learning teaches computers to think and learn as we do. Our brains recognize shapes, understand colors, and figure meaning together through layers. Deep Learning works similarly, but this time it will be using neural networks, mathematical equations, and computing power.

Say you're teaching it to identify the cats. Feed it many cat photos and it will recognize whiskers, tails, and ears. More photos, better at recognizing cats. But not just cats, you can teach it anything!

Why It Matters?

Because you can do a wide range of real-life tasks like predicting the weather, analyzing clothing reviews, classifying news, recognizing yoga poses, identifying fruits or detecting masks during epidemics, and more. And guess what, together we will see them all in this article.

Deep Learning is a helpful, super smart friend that keeps learning and can be applied to any field including data. By using its methods, not only you will be able to do decision-making predictions, but by using it, you also can taste different professions.

Deep Learning Project Ideas

You can find deep learning project ideas, through Github, and Kaggle kind of websites.

But for the sake of this article, I will do this task for you. For this article, I share with you 8 deep learning project ideas. By examining them not only you will be curious about them, I hope you can gain practical experience, let’s start.

Deep Learning Project #1: Weather Forecasting

Link to dataset: London weather dataset

Now, you are going to be a meteorologist and your task is to predict London’s weather.

Predicting it might be a challenging task, but not for you.

By using this London Weather data, now you will be able to use deep learning to forecast weather conditions and potentially tip agriculture, and tourism kind of sectors. To do this project, you should first explore the dataset. You can do this by using pandas and if you don’t know pandas that much yet, you can hone your skills by cracking real-life Python Pandas Interview questions here.

Now you know your data, manipulate it and transform it into the shape where you can build a neural net. Here you can build a recurrent neural network( RNN) on this historical data to predict future conditions. But don't forget to validate your model’s result on a testing set to ensure its performance.

Finally, you can predict the weather for a specific period of time, and I guess if it is rainy, that would not be much of a surprise!

Deep Learning Project #2: Predicting Sentiment from Clothing Reviews

Link to the dataset: Predicting Sentiment from Clothing Reviews

It is time to be Fashion Designer. Now the task is to read the clothing reviews. It might be your favorite or worst time due to the nature of the comments. You want to see them all, but there are thousands of comments made. Luckily, you have this dataset to predict sentiment from clothing reviews.

This is a text classification problem and can be solved by using Recurrent Neural Networks(RNNs) or maybe Transformer-based models like BERT. These are effective models for text classification and they can capture the sequential nature of the text.

First, as always, you should transform the text data, because generally, it will be in raw form. You have to make it ready to build Machine Learning models. Then you can build your model according to your taste and finally, it is time to evaluate your model on a separate test set.

You can try different flavors like hyperparameters to improve your model’s performance, but be sure you have enough computing power, otherwise, that process may last days.

Finally, you can analyze tens of thousands of reviews at a very limited time, thanks to the power of deep learning.

Deep Learning Project #3: Fake & Real News

Link to the dataset: Fake and Real News Dataset

Sometimes, It is really hard to believe news naively. That’s why you have been assigned to this task. Now you are a journalist and your task is to classify the news as fake or real. Of course, you can classify 10-15 or let’s say 20 news in a day, but what if you would use deep learning?

Fake and Real News Dataset includes a collection of new articles and these articles were labeled as either real or fake. The real thing is what we want to achieve by building Deep Learning to imitate human cognition and possibly try to increase its speed and accuracy if we can.

Here we are going to build RNN again. Our model then could learn how to identify real and fake news, and we can test its accuracy to be sure. Always remember to separate your dataset into training and test, to be sure that your model can make generalization well to unseen data.

Deep Learning Project #4: Yoga Pose Classification

Link to the dataset: Yoga Pose Classification

Now, let’s suppose you are a yogi and teaching it and your task is to classify yoga poses.

To do that, you first need to know which pictures symbolize yoga poses. This Yoga Pose Classification dataset includes a bunch of images of 5 main yoga poses, which are the downward dog pose, goddess pose, tree pose, plank pose, and warrior pose.

Of course, you possibly know which pose is which, yet you have to transform your knowledge to the computer to automate this classification process. To do that, CNN is a good fit, because this task includes an image classification task.

We can start this project by pre-processing again and we can use different methods. Let’s say we need to increase the diversity of our training data to increase efficiency, we should do data augmentation.

And finally, yoga poses can be classified automatically and you can do your asanas without being busy!

Deep Learning Project #5: Date Fruit Datasets - Image Classification

Link to the dataset: Date Fruit Dataset

So let’s imagine being a farmer and your task is to classify different types of fruits. It is a sunny day and you are in the middle of your garden, looking at different fruits. But how can you automate this process?

In this Date Fruit Dataset, there are images of seven different classes of data fruits exist : “: Barhee, Deglet Nour, Sukkary, Rotab Mozafati, Ruthana, Safawi, and Sagai, including their features like shape and colors.

By using this information and a little bit of Convolutional Neural Networks, you can guess what comes next in the next season, by just taking pictures of them and feeding our algorithm with that information.

Also, this approach can be used in the agricultural industry to increase efficiency and reduce manual labor. After developing this algorithm, by feeding it to the images and information of the local fruits, maybe you can earn some money too, by doing a little bit of marketing to the local firms.

Deep Learning Project #6: Face Mask Detection

Link to the dataset: Face Mask Detection

Now you are a doctor, and working in the hospital, during an epidemic, like Covid19. That’s why, now wearing a mask is mandatory to reduce an infection. So, it is time to detect masks from your patients.

But unfortunately, you should do this manually, because sometimes your patients might forget wearing these masks. Would not it be if there are a camera exist, near your door, this camera would detect face masks from your patients. Let them in if they wearing a mask, if not, they should wait longer.

But do that, first you need to develop a deep learning model that can detect whether a person is wearing a face mask or not This is a binary image classification problem that can be solved by using Convolutional Neural Networks.

In the following project, you can develop a face mask detection system using PyTorch and the Faster R-CNN model. You'll preprocess data, create a Dataset and DataLoader, and modify a pre-trained Faster R-CNN model for the task. After training and evaluating the model, you'll visualize its predictions.

Finally, you'll save the trained model for future use, maybe put that into the camera's algorithm and set the system, which automatically controls the patients whether they wearing masks or not.

Deep Learning Project #7: Celebrity Face Detection

Link to the Dataset: CelebFaces Attributes (CelebA) Dataset

Now let’s say you are a reporter and your task is to keep track of celebrities. So you should name them by just looking at them, right? If you are having trouble remembering one or two names, you can easily use this dataset.

You can use the CelebFaces Attributes (CelebA) Dataset, which has over 200K+ celebrity images. Your project would involve training a deep learning model, likely a Convolutional Neural Network (CNN), to identify various facial attributes.

To improve your model's performance, consider strategies like data augmentation, hyperparameter tuning, and regularization. You might also implement early stopping during training to prevent overfitting or use transfer learning by taking advantage of pre-trained models like VGG16 or ResNet.

Of course, after all these, you can use your model to predict the celebrities' names, by just uploading their pictures and asking the model to classify them.

Deep Learning Project #8: Mental Health FAQ Chatbot

Link to the dataset: Mental Health FAQ

Imagine if Sigmund Freud were born in this era. I guess he would have tried to develop a chatbot, for their patients to help them.

The Mental Health FAQ, a collection of frequently asked questions related to mental health, could be a great resource for this. Sigmund Freud’s task would be to train a deep learning model, such as a sequence-to-sequence model (a type of Recurrent Neural Network), to generate responses to mental health-related questions.

To enhance your model's performance, consider strategies like using attention mechanisms to better capture context, or experimenting with different types of RNNs like LSTM or GRU. Finally, this would help Dr. Sigmund Freud to serve their patients for unlimited time but of course with limited knowledge.

What is the difference between Machine Learning and Deep Learning?

In the beginning, you might have a hard time distinguishing between Machine Learning and Deep Learning. Basically, the algorithms that have been used, and their functionalities are different. I explained them all here in “Data Science vs Machine Learning vs Deep Learning” if you want to dig deeper.

In summary, machine learning is the cornerstone of deep learning, so before learning deep learning, having machine learning prior knowledge would have been much better for you.

Take your time and start with an easy machine learning project, if you don’t have much experience in Deep Learning, then you can climb the stairs through deep learning. Here I introduced a mix of Machine Learning and Deep Learning Projects.

Final Thoughts

It is actually funny, right? If you come closer from the right angle with curiosity and a good amount of eagerness towards anything, it won't take much time to learn or effort.

In this article, we will go through a bunch of exciting deep learning project ideas, along with datasets and a little bit of technical explanation, starting with weather forecasting to Freud’s mental heal chatbot.

If you have knowledge about Deep Learning, I suggest you take a look at these projects a little bit deeper. Also, if you are a beginner, there is no better time than today to start and here are 19 Data Science Project Ideas for beginners if you want to see more.

Thanks for reading!