Lecture
Programming Project: Image Recognition
Related lectures (29)
Image Recognition: Datasets and Algorithms
Explores a 2019 paper on image recognition, dataset challenges, biases, and the impact of large-scale datasets on deep learning models.
NFNets: Removing BatchNorm for High-Performance Image Recognition
Explores NFNets as an alternative to BatchNorm in ResNets, achieving high performance on ImageNet.
Machine Learning Fundamentals
Covers the fundamental concepts of machine learning, including classification, algorithms, optimization, supervised learning, reinforcement learning, and various tasks like image recognition and text generation.
Fine-Grained Visual Categorization: Challenges and Solutions
Explores challenges and solutions in fine-grained visual categorization, focusing on computer vision and machine learning.
Segmentation: Techniques and Applications
Explores segmentation techniques, including CNNs and U-Net models, for image recognition and analysis, emphasizing time-saving automated methods.
MaxPooling as inductive bias for images
Explores how MaxPooling enforces an inductive bias towards local translation invariance in convolutional neural networks.
Deep Learning Fundamentals
Introduces the fundamentals of deep learning, covering neural networks, CNNs, special layers, weight initialization, data preprocessing, and regularization.
Modern Convolutional Networks and Image Recognition
Explores the evolution of deep convolutional networks and their impact on image recognition accuracy.
Visual Intelligence: Machines and Minds
Explores visual intelligence, covering image formation, perception, computer vision, correspondence learning, motion analysis, and recognition in videos.
Visual Intelligence: Machines and Minds
Explores visual intelligence, image formation, computer vision, and representation understanding in machines and minds.