SUMMARY.md

Table of contents

• CS231n 2017 Spring Review
• [Lecture 5] Convolutional Neural Networks
• [Lecture 6] Training Neural Networks I
• [Lecture 7] Training Neural Networks II
• [Lecture 9] CNN Architectures
• [Lecture 10] Recurrent Neural Networks
• [Lecture 11] Detection and Segmentation
• [Lecture 12] Visualizing and Understanding
• [Lecture 13] Generative Models
• [Lecture 14] Deep Reinforcement Learning
• [Lecture 16] Adversarial Examples and Adversarial Training
• [MIT] Introduction to Human-Centered Artificial Intelligence (AI)
• [UCSanDiego] 3D Deep Learning Tutorial
• [Microsoft Research] An Introduction to Graph Neural Networks: Models and Applications
• [Paper Review 1] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• [Paper Review 2] You Only Look Once Unified, Real-Time Object Detection