陆昱宽

Sources:

1. The Normal/Gaussian Random Variable

Sources:

1. VAE paper
2. Mathematics Behind Variational AutoEncoders
3. Variational autoencoders by Matthew N. Bernstein
4. From Autoencoder to Beta-VAE
5. Stanford CS 228: The variational auto-encoder

Link: My VAE implementation on Github

Sources:

1. The Multivariate Gaussian Distribution

Sources:

1. Elements of Information Theory
2. An Introduction to Single-User Information Theory

Sources:

1. Elements of Information Theory
2. An Introduction to Single-User Information Theory

Sources:

1. Emerging Properties in Self-Supervised Vision Transformers (DINO)
2. PyTorch implementation and pretrained models for DINO

Sources:

1. ECS201A Homepage
2. ECS201A Projects(gem5)

All the experiments rely on the same python env and a software called gem5.

The professor has offered us github codespaces, such as classroom assignment-0, to develop,upon which you don't need to worry about the setup. So it's strongly recommended to develop with github codespaces.

However, if you prefer to develop on your local machine, to set up for the experiments you must:

1. Install and config gem5 on your system.
2. Clone the assignment repo and config it.

Sources:

1. --> How To Install Rust on Ubuntu 20.04

Sources:

1. How To Install Node.js on Ubuntu 20.04

Sources:

1. NvChad
2. NvChad --- Rocky Linux
3. Dreams of Code's NvChad playlist
4. BrunoKrugel's Github repo

Note: This article will only focus on NvChad itself. If you want to simply copy my computer configs (ALL OF THEM), you can refer to How to Set Up on A New Machine.