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Deep learning is a rapidly growing area of Artificial Intelligence which deals with analysis and design of automated computer based algorithms for yielding efficient representations of data at various abstraction levels. Deep learning is attaining unparalleled levels of accuracy—to the extent that the deep learning algorithms are able to overtake humans at classifying images and speech.
Computer vision is a research domain that stems from physics and pattern recognition and deals with the approaches about making computers gain high-level understanding (inference) from raw data in the form of images or videos.
This workshop will facilitate deep learning and computer vision researchers, professors and industry executives to share their expertise and knowledge with participants. It will offer a collaborative forum for discussion on recent and on-going developments, key issues and challenges, and practices in deep learning for computer vision.
Introduction to deep learning Fundamentals of Artificial Neural Networks, Key aspects of Pattern Recognition.
Gradient Descent algorithm, Back-propagation algorithm, Vanishing gradients, activation functions.
Deep learning libraries, regression models with Keras, Deep neural networks: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Auto encoders (Hands-on sessions on CNN, RNN and Auto Encoders).
Introduction to Computer Vision in Machines and Image Acquisition, Lighting, Camera, and Optics, Machine Vision Solution Strategies, Acquiring and Displaying Images, Getting Measurement-Ready Images.
Particle Analysis approach, Machine Vision Functions, Calibration for 2-D, Machine Vision Inspections, Considerations for Creating a Successful Vision Solution.
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