Lecture 9 | CNN Architectures
In Lecture 9 we discuss some common architectures for convolutional neural networks. We discuss architectures which performed well in the ImageNet challenges, including AlexNet, VGGNet, GoogLeNet, and ResNet, as well as other interesting models.
Keywords: AlexNet, VGGNet, GoogLeNet, ResNet, Network in Network, Wide ResNet, ResNeXT, Stochastic Depth, DenseNet, FractalNet, SqueezeNet
Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture9.pdf
--------------------------------------------------------------------------------------
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/
Видео Lecture 9 | CNN Architectures канала Stanford University School of Engineering
Keywords: AlexNet, VGGNet, GoogLeNet, ResNet, Network in Network, Wide ResNet, ResNeXT, Stochastic Depth, DenseNet, FractalNet, SqueezeNet
Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture9.pdf
--------------------------------------------------------------------------------------
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/
Видео Lecture 9 | CNN Architectures канала Stanford University School of Engineering
Показать
Комментарии отсутствуют
Информация о видео
11 августа 2017 г. 22:03:13
01:17:40
Другие видео канала
Lecture 10 | Recurrent Neural NetworksL13/7 Beyound ResNetLecture 8 | Deep Learning Software10. AlexNet - CNN Explained and Implemented.Lecture 39 : Popular CNN Architecture: VGG16, Transfer LearningDeep Generative Modeling | MIT 6.S191C4W3L08 Anchor Boxes9. VGG16 architecture and implementationBut what is a Neural Network? | Deep learning, chapter 1Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition[Classic] Deep Residual Learning for Image Recognition (Paper Explained)C4W3L01 Object LocalizationLecture 5 | Convolutional Neural NetworksLecture 11 | Detection and Segmentation2 ResNet ArchitectureLecture 39 Popular CNN Architecture VGG16, Transfer LearningResNet Architecture: Part 1 (in Hindi)How Convolutional Neural Networks work