This course provides a comprehensive exploration of deep learning and visual recognition, covering key concepts, techniques, and models in the field. The curriculum includes:
- Deep Learning Fundamentals: Introduction to the basics of deep learning, including image classification and linear classifiers.
- Optimization and Neural Networks: Dive into optimization techniques, backpropagation, and the foundations of neural networks.
- Convolutional Neural Networks (CNNs): Comprehensive study of CNNs, their architecture, and applications in image recognition.
- Advanced Neural Network Techniques: Exploration of activation functions, weight initialization, normalization, and transfer learning.
- Regularization and Optimizers: Understanding various regularization techniques and optimizers in deep learning.
- Visual Recognition: Detailed analysis of object detection and segmentation techniques.
- Recurrent Neural Networks and Transformers: Study of RNNs, attention mechanisms, and transformers, including their application in vision transformers.
- Generative Models: Insight into generative models, including autoregressive models, Variational AutoEncoders, Generative Adversarial Networks, and diffusion models.
- Visualization and Stylization: Techniques for visualizing and stylizing deep learning models.
- Self-supervised Learning: An emerging area of deep learning focusing on learning without explicitly labeled data. This course is structured to provide a deep understanding of both the theoretical aspects and practical applications of deep learning in visual recognition, making it suitable for students and researchers interested in the cutting-edge developments in AI and computer vision.