Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition
57:56Lecture 2 | Image Classification
59:31Lecture 3 | Loss Functions and Optimization
1:14:40Lecture 4 | Introduction to Neural Networks
1:13:59Lecture 5 | Convolutional Neural Networks
1:08:56Lecture 6 | Training Neural Networks I
1:20:19Lecture 7 | Training Neural Networks II
1:15:30Lecture 8 | Deep Learning Software
1:18:07Lecture 9: CNN Architectures (CS231n)
1:17:40Lecture 10: Recurrent Neural Networks (CS231n)
1:13:09Lecture 11: Detection and Segmentation (CS231n)
1:14:26Lecture 12: Visualizing and Understanding (CS231n)
1:15:48Lecture 13: Generative Models (CS231n)
1:17:41Lecture 14: Deep Reinforcement Learning (CS231n)
1:04:01Lecture 15: Efficient Methods and Hardware for Deep Learning (CS231n)
1:16:52Lecture 16: Adversarial Examples and Adversarial Training (CS231n)
1:21:46Lecture 13: Generative Models (CS231n)
This lecture introduces generative modeling as a form of unsupervised learning. It elaborates on autoregressive models like PixelRNN/PixelCNN, traditional and variational autoencoders (VAEs), and generative adversarial networks (GANs).
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
Lecture 12: Visualizing and Understanding (CS231n)
Next · Lecture 14Lecture 14: Deep Reinforcement Learning (CS231n)
Seminar notes & discussion
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