Course description
This introductory course focuses on deep learning, covering theoretical aspects and network architectures. Topics include Neural Networks, Optimization, Convolutional Neural Networks, Generative Models, RNNs and advanced techniques like Transformers.
In labs, students gain hands-on experience with Python, PyTorch, Image Classification, Transfer Learning, Object Detection, Sequence Models, NLP, Advanced Vision and Generative Models. The course concludes with a project presentation and competition.
Project/Competition
Students will work on machine learning projects (in groups of 2) to demonstrate their programming and deep learning skills mastered throughout the course. During the final labs, a class competition will be held and students will present projects in front of a committee. The winning team will be eligible for prize.
How to win 20 000 CZK?Course Schedule
Lectures and labs are scheduled on Monday mornings between 9:00am and 12:00pm and are held in the Institute of Information Theory and Automation (UTIA), room 203.
Our Team
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Reviews
Welcome to our Testimonials section, a collection of student reviews that reflect their experiences and insights. Our students have shared their honest feedback about the course, teaching style, project presentation, exam and overall experience.