What role do loss functions such as Mean Squared Error (MSE) and Cross-Entropy Loss play in training RNNs, and how is backpropagation through time (BPTT) used to optimize these models?
Tuesday, 11 June 2024 by EITCA Academy
In the domain of advanced deep learning, particularly when dealing with Recurrent Neural Networks (RNNs) and their application to sequential data, loss functions such as Mean Squared Error (MSE) and Cross-Entropy Loss are pivotal. These loss functions serve as the guiding metrics that drive the optimization process, thereby facilitating the learning and improvement of the
- Published in Artificial Intelligence, EITC/AI/ADL Advanced Deep Learning, Recurrent neural networks, Sequences and recurrent networks, Examination review
Tagged under: Artificial Intelligence, BPTT, Cross-entropy Loss, Gradient Descent, Loss Functions, Mean Squared Error
How does the concept of Intersection over Union (IoU) improve the evaluation of object detection models compared to using quadratic loss?
Wednesday, 22 May 2024 by EITCA Academy
Intersection over Union (IoU) is a critical metric in the evaluation of object detection models, offering a more nuanced and precise measure of performance compared to traditional metrics such as quadratic loss. This concept is particularly valuable in the field of computer vision, where accurately detecting and localizing objects within images is paramount. To understand
- Published in Artificial Intelligence, EITC/AI/ADL Advanced Deep Learning, Advanced computer vision, Advanced models for computer vision, Examination review
Tagged under: Artificial Intelligence, Bounding Box, Evaluation Metrics, IoU, Loss Functions, Object Detection