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Lecture 13 | Generative Models

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In Lecture 13 we move beyond supervised learning, and discuss generative modeling as a form of unsupervised learning. We cover the autoregressive PixelRNN and PixelCNN models, traditional and variational autoencoders (VAEs), and generative adversarial networks (GANs).

Keywords: Generative models, PixelRNN, PixelCNN, autoencoder, variational autoencoder, VAE, generative adversarial network, GAN

Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture13.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 โ€œdeep learningโ€) 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/