Top 5 Deep Learning Project Ideas for a New Career in 2022

If you are already taking your first steps in machine learning, the best option for your next portfolio project will be deep learning (DL). As DL imitates a particular kind of network, the human brain, it has a wide scope of application in business and other industries to automate or optimize the analysis and forecasts.

Before you start reading about the best 2022 ideas for deep learning projects, we suggest you dive into deep learning and neural networks guide first. In the article, you will find crucial deep learning definitions, mechanics, and applications. Besides, there you’ll find illustrated differences between machine learning and deep learning, which are vital for the young professional to absorb.

So, let’s move on to the project ideas that will make your 2022 portfolio stand out.

  1. Face recognition project

Face recognition is a subfield of object detection technology in deep learning. Human face detection in real-time is widely used in many countries today. It also gets more meaningful, as it helps find missing persons, protects schools from threats, and communicates emotions to blind people. Face recognition is suitable for beginners and can be implemented with Python and OpenCV.

  1. Music classification tool

The music industry is one of the most prosperous elements of the business world. With the emergence of more streaming platforms, and open-talent platforms, such as Soundcloud, it has become even more significant in our society. People are not afraid to share their talent and new visions on music genres. The good news is that it has also become a good place to test your deep learning skills! You can find a free dataset of high-quality audio tracks and build a deep learning model that will classify the tracks into genres. This task will help you deliver a useful classifying technology, and test your skills in extracting information from the audio, such as MFCC, spectrograms, and more.

  1. Lung cancer detection

Cancer detection technologies use a variety of image recognition algorithms, which are usually prone to mistakes and time-consuming. A lot of ML professionals focus on improving this situation. One example is a 12 Sigma lung cancer detection algorithm, which has reduced the time of lung cancer detection to two minutes. By focusing on reducing the time of cancer detection alone, you can help increase survival rates among cancer patients.

  1. Visual tracking

Visual tracking systems have found their application in a variety of industries, from security and surveillance to augmented reality. You can imagine its application in any task, which includes camera usage. You can test your deep learning skills to detect and analyze moving objects in a video frame. As a result, you will need to make sure your deep learning model can locate the objects, filter, and associate them.

  1. Image captions generation

Even from the title, you can already conclude that image caption generation works both with images and text. To complete a deep learning model on image caption generation you will need to learn the basics of image recognition and natural language processing. The procedure is very straightforward. First, your model will need to analyze the image and understand its context. Second, it will need to generate a caption for it in any human language. LSTM and CNN will be the best option for this task.

Needless to say, some industries and subfields need deep learning professionals, who can implement all of these algorithms for real-life problems. If you add all of these to your 2022 portfolio, you will have a great chance of engaging in new meaningful, and innovative projects.