I'm a Dutch robotics engineer with a passion for deep learning and computer vision. I recently reproduced the paper EfficientNetV2: Smaller Models and Faster Training and have shared my code in a GitHub repository. I'm constantly learning and exploring new technologies, and I'm excited about the potential of machine learning to revolutionize the field of robotics.

• Reproducing other cutting-edge deep learning papers and experimenting with new architectures and techniques
• Building projects that integrate computer vision and robotics, such as object recognition and autonomous navigation
• Developing my skills in software engineering and agile methodologies to build reliable and scalable systems

• Reinforcement learning and how it can be applied to robotics
• Advanced computer vision techniques such as multi-object tracking and 3D reconstruction
• Cloud computing and distributed systems for scalable machine learning

• Check out my portfolio website to see some of my past projects
• Connect with me on LinkedIn
• Email me on Email

I'm seeking a challenging role as a computer vision engineer, ideally with a forward-thinking company in the robotics or autonomous vehicle space. I'm particularly interested in opportunities with Tesla, where I can contribute to cutting-edge research and development.

• Programming languages: Python, C++, MATLAB
• Deep learning frameworks: TensorFlow, PyTorch, Keras
• Robotics and control systems: ROS, Gazebo, Simulink
• Cloud computing: GCP

• Development of PLAEX's computer vision system for Garby

Thanks for stopping by my GitHub profile! I'm always happy to connect with other robotics and AI enthusiasts, so don't hesitate to reach out.

1. Make requirements less dumb, i.e, reduce to the "one" purpose
2. Delete everything in the system that is not absolutely needed
3. Re-add what is still needed. If you don't have anything to re-add you did not delete enough.
4. Speed it up, make more efficient
5. Automate