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 12: Visualizing and Understanding (CS231n)
This session delves into techniques for interpreting internal mechanisms of CNNs, such as saliency maps and feature inversion. It also covers generative applications like DeepDream and neural style transfer, showcasing image synthesis.
In Lecture 12 we discuss methods for visualizing and understanding the internal mechanisms of convolutional networks. We also discuss the use of convolutional networks for generating new images, including DeepDream and artistic style transfer.
Keywords: Visualization, t-SNE, saliency maps, class visualizations, fooling images, feature inversion, DeepDream, style transfer
Lecture 11: Detection and Segmentation (CS231n)
Next · Lecture 13Lecture 13: Generative Models (CS231n)
Seminar notes & discussion
No public seminar notes on this lecture yet.