Thermal Comfort Monitoring: Non-Intrusive Solutions

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Page 3 of 4

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Explores visual intelligence, robotics perception, and multi-task learning techniques in computer vision.

Explains image recognition in LabVIEW, focusing on reading parameters and recognizing digits.

Covers the impact of transformers in computer vision, discussing their architecture, applications, and advancements in various tasks.

Covers the basics of machine learning for physicists and chemists, focusing on image classification and dataset labeling.

Discusses Generative Adversarial Networks and their applications in synthesizing data and generating images.

Explores machine visual perception, weakly-supervised learning, and future research in intelligent systems.

Introduces the course project for CS-503, emphasizing the importance of project selection related to vision and perceptual agents.

Covers PyTorch tensor data structure and training a CNN to classify images.

Covers the No Free Lunch Theorem and the role of inductive bias in deep learning and reinforcement learning.