Deep Learning for Computer Vision
Live online classroom led by experts in Autonomous Driving
What to expect from this training?
You will gain the skills needed to solve real-world perception problems like object detection or instance segmentation, that arise in Autonomous Driving or medical imaging, using a good combination of Deep Learning and Computer Vision.
This course is ~70-80% hands-on. You will learn theory while getting your hands dirty and writing code from scratch. We will solve interesting real-world Computer Vision and NLP problems.
Your network performance is as good as your data set. At the beginning, I struggled a lot with preparing and visualizing the training data. Since this step is potentially painful, we dedicate 2 chapters for this alone.
You will obtain guided hands-on experience using the most widely used, industry-standard software, tools, and frameworks, such as OpenCV, TensorFlow 2, Keras, DIGITS, TensorRT, NumPy, Pandas and many more. We will work with the latest versions of these frameworks, implementing interesting exercises.
Doing AI on a strong station is quite different than doing it on an embedded device. When moving to embedded, you need to use as little memory as possible, as little processing power as possible, and at the same time, you need to maintain the accuracy of the network. You will learn how to cope with these challenges on a real embedded device.
Edocti is actively involved in industrial Autonomous Driving projects. There are autonomous vehicles out there with software written by us. You will earn Edocti's DLCV certification to prove your subject matter competency and support professional career growth.
Is this training right for me?
What equipment do you need?
Learn with the best
Paul Ianas
CEO, Autonomous Driving expert
With over 5000 hours of training, Paul is specialized in AI at the edge and IIoT. His area of expertise includes operating systems, deep learning and computer vision, modern C++ and Python. In 2016 Paul founded Edocti, Romania's private research company specialized in Autonomous Driving and V2X. Since 2016 he contributed with R&D to a series of Autonomous Driving projects for Volvo, Kia, VW and GM. When he doesn't work in AD projects, he teaches a series of well-regarded courses, ranging from IoT, Deep Learning & Computer Vision, to Linux, real time operating systems and modern C++.
Petru Radu
PhD in image processing, DL & CV researcher
In 2013 Petru obtained his PhD in image processing at the University of Kent, U.K. where he continued as a Postdoc in Image Processing and Document Analysis. He was a Postdoctoral Research Associate in an EU funded project at University of Reading, U.K, working on biometric recognition. Author of over 20 research papers and 6 invention patents, Petru's research interests include statistical pattern recognition, deep learning, image processing and multiple classifier systems. He is also an associate lecturer in computer vision at West University of Timisoara.