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/
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
--------------------------------------------------------------------------------------
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/
![](https://i.ytimg.com/vi/vT1JzLTH4G4/mqdefault.jpg)
Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition
Lecture 1 gives an introduction to the field of computer vision, [...]
![](https://i.ytimg.com/vi/OoUX-nOEjG0/mqdefault.jpg)
Lecture 2 | Image Classification
Lecture 2 formalizes the problem of image classification. We discuss [...]
![](https://i.ytimg.com/vi/h7iBpEHGVNc/mqdefault.jpg)
Lecture 3 | Loss Functions and Optimization
Lecture 3 continues our discussion of linear classifiers. We introduce [...]
![](https://i.ytimg.com/vi/d14TUNcbn1k/mqdefault.jpg)
Lecture 4 | Introduction to Neural Networks
In Lecture 4 we progress from linear classifiers to fully-connected [...]
![](https://i.ytimg.com/vi/bNb2fEVKeEo/mqdefault.jpg)
Lecture 5 | Convolutional Neural Networks
In Lecture 5 we move from fully-connected neural networks to [...]
![](https://i.ytimg.com/vi/wEoyxE0GP2M/mqdefault.jpg)
Lecture 6 | Training Neural Networks I
In Lecture 6 we discuss many practical issues for training modern [...]
![](https://i.ytimg.com/vi/_JB0AO7QxSA/mqdefault.jpg)
Lecture 7 | Training Neural Networks II
Lecture 7 continues our discussion of practical issues for training [...]
![](https://i.ytimg.com/vi/6SlgtELqOWc/mqdefault.jpg)
Lecture 8 | Deep Learning Software
In Lecture 8 we discuss the use of different software packages for [...]
![](https://i.ytimg.com/vi/6niqTuYFZLQ/mqdefault.jpg)
Lecture 10 | Recurrent Neural Networks
In Lecture 10 we discuss the use of recurrent neural networks for [...]
![](https://i.ytimg.com/vi/DAOcjicFr1Y/mqdefault.jpg)
Lecture 9 | CNN Architectures
In Lecture 9 we discuss some common architectures for convolutional [...]
![](https://i.ytimg.com/vi/nDPWywWRIRo/mqdefault.jpg)
Lecture 11 | Detection and Segmentation
In Lecture 11 we move beyond image classification, and show how [...]
![](https://i.ytimg.com/vi/nDPWywWRIRo/mqdefault.jpg)
Lecture 11 | Detection and Segmentation
In Lecture 11 we move beyond image classification, and show how [...]
![](https://i.ytimg.com/vi/6wcs6szJWMY/mqdefault.jpg)
Lecture 12 | Visualizing and Understanding
In Lecture 12 we discuss methods for visualizing and understanding the [...]
![](https://i.ytimg.com/vi/5WoItGTWV54/mqdefault.jpg)
Lecture 13 | Generative Models
In Lecture 13 we move beyond supervised learning, and discuss [...]
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Lecture 14 | Deep Reinforcement Learning
In Lecture 14 we move from supervised learning to reinforcement [...]
![](https://i.ytimg.com/vi/eZdOkDtYMoo/mqdefault.jpg)
Lecture 15 | Efficient Methods and Hardware for Deep Learning
In Lecture 15, guest lecturer Song Han discusses algorithms and [...]
![](https://i.ytimg.com/vi/CIfsB_EYsVI/mqdefault.jpg)
Lecture 16 | Adversarial Examples and Adversarial Training
In Lecture 16, guest lecturer Ian Goodfellow discusses adversarial [...]