![]() ![]() Reduced registration for young professionals (up to 2 years after graduation): 55 Euros.Unemployed or Undergraduate/MSc/PhD student*: 25 Euros.Reduced registration for young professionals (up to 2 years after graduation): 50 Euros.They should belong to one of to AIDA members. The AIDA Students are entitled 50% discount for Course registration in this course. Unemployed or Undergraduate/MSc/PhD student*: 60 EuroĪfter the completion of your payment, please fill in the form below:.Reduced registration for young professionals (up to 2 years after graduation): 110 Euros.Unemployed or Undergraduate/MSc/PhD student*: 50 Euros.Reduced registration for young professionals (up to 2 years after graduation): 100 Euros.** This programme is indicative and may be modified without prior notice by announcing (hopefully small) changes in lectures/lecturers. * Eastern European Summer Time (EEST, UTC+3 hours) PyTorch: Understand the core functionalities of an object detector. Parallel GPU and multi-core CPU architectures – GPU programming The course link is the following: PROGRAM Date/time* All lectures and workshops will be delivered remotely using Zoom. Finally, instructions on how to install AirSim can be found here. The participants are also required to own a Google account for the workshops exercises. A standard PC with a stable internet connection is required. HOW?Įach registrant will use her/his own computer for a) participating in the course and b) for running the programming exercises. You can find additional information about the city of Thessaloniki and details on how to get to the city here. WHERE? All lectures and workshops will be delivered remotely. The course will take place on 24-26 August 2022. Part C (8 hours), Autonomous UAV cinematography OpenCV programming for object tracking.PyTorch: Understand the core functionalities of an object detector.Deep learning for object/face detection.Part B (8 hours), Deep Learning for Computer Vision Parallel GPU and multi-core CPU architectures – GPU programming.PDF files will be available at the end of the course. Lectures and programming workshops will be in English. The lectures and programming tools will provide programming skills for the various computer vision and deep learning problems encountered in autonomous systems and autonomous drone applications, e.g., drone cinematography, drone inspection, land/marine surveillance, search & rescue, and 3D modeling. Additionally, participants will have the opportunity to understand video summarization techniques, which can be used to autonomously distill the important parts of the recorded video. Such simulations will be presented using AirSim. Before mission execution, it is best simulated, using drone mission simulation tools. Part C lectures will focus on autonomous UAV cinematography. The hands-on programming workshop will be on target detection with Pytorch and on how to use OpenCV (the most used library for computer vision) for target tracking. Part B lectures will focus on deep learning algorithms for computer vision, namely on 2D object/face detection and 2D object tracking (giving the attendants the opportunity to master state of the art object detectors and video trackers). The first one will be on image classification using CNNs, while the second one will be on CUDA programming, focusing on 2D convolution algorithms. ![]() Two programming workshops will take place. Also, parallel GPU and multi-core CPU architectures commonly used to train DNNs will be presented. The lectures of this part provide a solid background on Deep Neural Networks (DNN) topics, notably convolutional NNs (CNNs) and deep learning for image classification. Part A will focus on Deep Learning and GPU programming. The short course consists of three parts (A,B,C), each having lectures and programming workshops with hands-on lab exercises. ![]() The target application domains are autonomous systems (e.g., drone cinematography) and digital/social media. This three day short course and workshop provides an in-depth presentation of programming tools and techniques for various computer vision and deep learning problems. ![]()
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