[This week Randy will be presenting Lecture 13: Generative Models from the 2017 series. https://www.youtube.com/watch?v=5WoItGTWV54

"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)."

This is also Lecture 12 of the 2021 series: http://cs231n.stanford.edu/slides/2021/lecture_12.pdf ]

We are going through Stanford's lecture series Convolutional Neural Networks for Visual Recognition (CS231n) (http://cs231n.stanford.edu/)
This series is for those who want to take a deep dive into deep learning for computer vision tasks and/or to brush up on the fundamentals:

"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."

The idea will be to watch each lecture ahead of meeting. A lead will guide us through the content of the lecture, summarizing important or interesting concepts. We'll do this remotely using video conferencing. A link will be posted a few minutes before each meeting.

For up to the minute info on this event please visit the Meetup page.