![]() You should know basics of probabilities, gaussian distributions, mean, standard deviation, etc. Basic Probability and Statistics (e.g.You should be comfortable taking derivatives and understanding matrix vector operations and notation. ![]() College Calculus, Linear Algebra (e.g.C/C++/Matlab/Javascript) you will probably be fine. If you have a lot of programming experience but in a different language (e.g. Through multiple hands-on assignments and the final course project, students will acquire the toolset for setting up deep learning tasks and practical engineering tricks for training and fine-tuning deep neural networks.Īll class assignments will be in Python (and use numpy) (we provide a tutorial here for those who aren't as familiar with Python). Additionally, the final assignment will give them the opportunity to train and apply multi-million parameter networks on real-world vision problems of their choice. During the 10-week course, students will learn to implement and train their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. This course is a deep dive into the details of deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars.
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