Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition
Lecture 1 gives an introduction to the field of computer vision, discussing its history and key challenges. We emphasize that computer vision encompasses a wide variety of different tasks, and that despite the recent successes of deep learning we are still a long way from realizing the goal of human-level visual intelligence.
Keywords: Computer vision, Cambrian Explosion, Camera Obscura, Hubel and Wiesel, Block World, Normalized Cut, Face Detection, SIFT, Spatial Pyramid Matching, Histogram of Oriented Gradients, PASCAL Visual Object Challenge, ImageNet Challenge
Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture1.pdf
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Convolutional Neural Networks for Visual Recognition
Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.
Website:
http://cs231n.stanford.edu/
For additional learning opportunities please visit:
http://online.stanford.edu/
Lecture 1 gives an introduction to the field of computer vision, discussing its history and key challenges. We emphasize that computer vision encompasses a wide variety of different tasks, and that despite the recent successes of deep learning we are still a long way from realizing the goal of human-level visual intelligence.
Keywords: Computer vision, Cambrian Explosion, Camera Obscura, Hubel and Wiesel, Block World, Normalized Cut, Face Detection, SIFT, Spatial Pyramid Matching, Histogram of Oriented Gradients, PASCAL Visual Object Challenge, ImageNet Challenge
Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture1.pdf
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Convolutional Neural Networks for Visual Recognition
Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.
Website:
http://cs231n.stanford.edu/
For additional learning opportunities please visit:
http://online.stanford.edu/
