Using machine learning, computer vision and object recognition, dida succeeded in developing a software to automatically plan solar based on satellite data.
Machine learning in production: We did the development of AI-based optical defect detection software for the semiconductor laser production.
Machine learning and security systems: Development of a multi-level system with facial recognition and automated access control using AI.
By Emilius Richter • July 18th, 2022
For a software provider, the project proposal is the first step toward meeting the needs of the customer. In this article, I will describe the most important modules in machine learning project proposals.
By Frank Weilandt (PhD) • July 16th, 2021
Read here about deep learning based computer vision techniques for information extraction from .xploded-view technical drawings-
By Fabian Gringel • June 21st, 2021
Contrastive language image pretraining (CLIP): Read about the functionality as well as applications of the CLIP model, a zero-shot image classifier.
By Emilius Richter • May 21st, 2021
Read about the 21 relevant questions that should be considered & answered upfront to start a successful machine learning software project.
By Dmitrii Iakushechkin • April 8th, 2021
Read here about the best image annotation tools for computer vision tasks. Everything from the installation to illustration with an example.
By Lovis Schmidt • August 17th, 2020
Comparison of the open source Python PDF text extraction tools PyPDF2, pdfminer & PyMuPDF. Read about tools for extracting text from PDF files here.
By Matthias Werner • August 3rd, 2020
Learn more about multi-agent reinforcement learning (MARL), its capabilities, limitations, and how MARL can be viewed as a problem solver.