Given the immense competition in Deep Learning and Artificial Intelligence job roles and the application scope as a whole in the different sectors of modern technology, it should be a passion for aspirants to keep learning and implementing the learned concepts through a few projects. Learning Python is one of the most important steps for beginners. You can learn Python Online. With that, let us look at a few Deep Learning projects for 2023 that you can lay your hands on arranged in the ascending order of their difficulty.
Cifar 10
It involves the Cifar 10 dataset which consists of color images of 10 different objects like birds, aeroplanes, dogs, and other objects and your task is to correctly classify them.
It is a good beginner’s project where instead of just using a feed-forward neural network, one could learn how to use a convolutional neural network.
This is included or built-in in PyTorch and TensorFlow, so you don’t have to worry about loading the data.
Cats and Dogs
The cats and dogs dataset can be found on Kaggle. As the name suggests, this dataset consists of images of only cats or dogs, and this task are simpler than the previous one but now the challenging part could be:
- To learn how to download the data and then load it in the correct format
- And give it to your model and to get a really good performance, you might try out a technique that is called Transfer Learning.
Breast Cancer Classification
If someone wants to specialize in medical image and data processing, the breast cancer classification project must be on their portfolio. One of the most common applications of deep learning is in medicine.
If a search is made on Kaggle, it can be found that many challenges deal with medical data. You must train the model and accurately classify cancer sub-types depending on 2-D medical photos in this assignment.
Disaster Tweet Project
Up until now, we had three computer vision projects. Now let us switch the topic to Natural Language Processing. This project is about classifying whether or not a Twitter tweet is about a real-world disaster.
For this assignment, it is a great way to learn both Recurrent Neural Networks as well as Long Short Term Memory. These are two special types of neural networks that perform well with text data.
Chatbot
Next, I suggest a project almost everyone will enjoy and that is building your chat-bot from scratch and putting it to test with a simple example application. To get data for this task, you can take help from two open-source datasets.
- The first one is the Conversational Question Answering Dataset provided by Stanford NLP.
- And the second one is the Google Natural Question dataset.
Recommender Systems
All the tech giants like Spotify, Twitter, YouTube, etc. need a recommender system. So, depending on the data they’ve gathered about the user, they’d like to recommend more stuff that the user might love. To get a solid understanding, I would suggest getting your hands dirty with a movie recommender system. For this particular project, you can either use:
- The 100k MovieLens dataset.
- You can use the official Netflix dataset that you can find on Kaggle.
This seems to be an excellent time to learn about the Collaborative Filtering approach.
Forecasting
This is another interesting field where we deal with time series data and is a project where you can practice your knowledge of RNNs. Here, we want to predict the values of a time series in the future and a very popular example is the stock price prediction. To use data for this project, we would recommend scraping or downloading your own data from Yahoo finance.
This isn’t too difficult, although there is a specialized Python module called YFinance that you really can use straight away. You can use the data up to a certain point in the past to train your model and then evaluate it with the data up to the present date and then with this you can build your model to predict the price in the future.
Object Detection
For the last two projects, there are two advanced computer vision tasks. The first task is object detection where the goal is to identify the specified objects in an image or a video and then mark them, for example, draw the bounding box. So you have to check if the object exists and if it does, then you have to mark it and you even have to deal with multiple different objects in an image. So this is indeed a very advanced task.
You could implement this from scratch. For example, you could try to implement the Yolo object detection model from scratch but it is recommended to just use a pre-trained model for this. You also have to learn about OpenCV here. You can use the raccoon dataset or the annotated driving dataset.
Style transfer
For the last project, I’d suggest having a look at Style transfer. This is just another fascinating application of Deep Learning. We train the model here, and then perhaps we can feed it one style image, which it can thereafter apply to every new image we provide it. You don’t have to implement this on your own again here. I recommend using an existing project.
For example, there is the TensorFlow fast style transfer project on Github or the Pytorch fast neural style implementation. So both are just fine. But then to retrain your model for your style images, you have to use the coco dataset which is a large scale object detection, segmentation, and captioning dataset and this is one of the most important deep learning datasets for computer vision, so you should check this out.
Conclusion
On a concluding note, all the projects and the datasets involved will see you through your journey of practicing training of your deep learning model right from the beginner level to the advanced level while you learn to code complex models. Once you get the gist of it, you will learn to employ ways to optimize the results of your models.
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