Machine learning and deep learning are still in the experimental stage of development. However, machine learning capabilities are already routinely incorporated in software for both personal and business use. You can find good machine learning projects everywhere – from home and office automation tools through industrial equipment to mobile devices.
Machine learning ideas drive mostly projects aimed at the development of smart algorithms like artificial intelligence. Even so, do not make the mistake of referring to a machine learning project as “artificial intelligence”. Machine learning algorithms are mainly about the application of logical operators of the type “if…then,” which does not make them less amazing or challenging to implement in practice.
Deep Learning Projects You Can Try Out
AirSim By Microsoft
AirSim is a platform enabling researchers to study autonomous vehicle behavior and apply reinforcement learning algorithms to experiment with aerial drones. This open-source project is cross-platform and creates realistic and visually stunning simulations for any researcher to test his or her skills in the field of control over drones in the air. You can control also cars in the virtual environments AirSim creates.
The machine learning project is using the Unreal Engine. Moreover, it supports popular flight controllers such as PX4. Alternatively, engineers can use APIs to interact with the self-driving vehicle.
It is one of those machine learning projects in Python language. Furthermore, you can also access APIs through other programming languages such as C++, C#, and Java.
Detectron By Facebook
Detectron is a project that is trying to stimulate machine learning ideas in the field of object detection and develop relevant algorithms. It is a machine learning project in Python that aims at supporting fast implementation and evaluation of novel research in the field of object detection.
Detectron is using the Caffe2 deep learning framework. Likewise, it relies on advanced object detection algorithms such as Mask R-CNN, RetinaNet, Faster R-CNN, and others to produce novel results.
Vid2vid By NVIDIA
NVIDIA enters the list with a “photorealistic video-to-video translation,” which is another example of how extravagant machine learning ideas are.
If you wonder what is video-to-video or image-to-image translation and what is its use, here are the possible applications of such deep learning algorithms:
– Turning semantic label maps into photo-realistic videos,
– Synthesizing people talking from edge maps,
– Generating human motions from poses.
The project runs on Linux and macOS and requires Python 3 as well as NVIDIA GPU + CUDA cuDNN and PyTorch 0.4.
Planet-Hunting By Google Brain
If you are looking for machine learning ideas that can re-shape our world and open up new horizons to the humankind, you should explore the planet-hunting machine learning project by Google Brain, Google’s AI department.
The project tries to train neural networks to analyze data from NASA’s Kepler space telescope. After that, it identifies the most promising planet signals.
There are automated software solutions like the Kepler data processing pipeline, detecting signals from planet candidates. However, researchers still need to manually tell real planets from false positives.
Machine learning projects like this one can occupy your mind in the long-term. Kepler has observed some 200,000 stars so far. Google’s algorithm was able to search for planets orbiting. However, there are only 670 of them as of now. There are still many challenges related to projects like this. The challenges include filtering false positives since neural networks, like automated software, produce results you need to process manually to make sure you have a planet in place.
DeepGlint Backed By Hyundai
China‘s DeepGlint is one of the leading companies in the field of object recognition and behavior pattern analysis. It uses deep learning and artificial intelligence to recognize motion patterns.
The automaker, Hyundai, wanted to develop in-vehicle facial recognition and behavior pattern analysis technology, so it funded DeepGlint on its recent research. DeepGlint has one of the very good machine learning projects in the field. Their software is able to recognize in one second the face of a target person among one billion people from a distance of 50 meters.
One of the stated ultimate goals of DeepGlint is to build a visual sensor network that understands what’s happening around it. This is one of many promising machine learning ideas whose development you can only watch as the project is not open for outside researchers.
Importance Of Accessibility To Data And Research
If you are looking for promising machine learning ideas and machine learning projects, you should explore areas such as self-driving vehicles and automated transportation (logistics included), personalization of online services as well as smart homes and offices.
Digital assistants and “smart” devices are the most visible result of the application of machine learning ideas into practice. However, these projects are backed by large multinationals. And so, these are not available to the general public. Further, you need access to vast amounts of data for a machine learning project to be successful. You need to look for projects where you have access to publicly accessible data. Good machine learning ideas are everywhere. However, finding a good machine learning project that is readily available for anyone to join needs some research.
If you are interested in deep learning projects, remember that a good number of machine learning projects are not interesting on the surface. Furthermore, they bear the huge potential for possible uses at a future stage. Video-to-video translation that generates human body motion from static poses or a visual sensor network that is capable of understanding what is going on within its reach holds huge potential for building intelligent systems in the future.
Machine learning and artificial intelligence are not only interesting fields to solve challenging problems. Nevertheless, they are also very lucrative from a business point of view. Good machine learning projects that look like pure academia find real-life applications in markets worth billions of dollars in annual revenue easily. Examples are office and home automation tools you can find everywhere. However, deep learning algorithms are yet to unleash their full business potential across a variety of household and office applications.
