Deep Learning for Computer Vision | UPC Barcelona

Comprehensive course on deep learning for computer vision, covering CNN, RNN, GAN, and state-of-the-art models for image and video analysis.

Introduction

This course provides a comprehensive introduction to deep learning for computer vision, covering the fundamental concepts, techniques, and applications of deep learning in the field of image and video analysis.

Highlights

• Covers a wide range of deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs)
• Explores the latest advancements and state-of-the-art models in computer vision tasks such as image classification, object detection, semantic segmentation, and image generation
• Includes hands-on coding exercises and projects using popular deep learning frameworks like TensorFlow and PyTorch
• Taught by experienced researchers and practitioners from the Universitat Politècnica de Catalunya (UPC) in Barcelona

Recommendation

This course is highly recommended for students, researchers, and professionals interested in leveraging deep learning techniques for computer vision applications. It provides a solid foundation in deep learning concepts and their practical implementation, making it suitable for both beginners and those with prior experience in the field.

YouTube Videos