While there are tons of materials about all kinds of Deep Learning knowledge, frameworks and configurations, I feel overwhelmedby them when I am doing research. I started from TensorFlow, then switched to PyTorch. Now when I am doing anchor-free object detections and feature fusion experiments in object detections, I need to learn Detectron2, maybe mmDetection as well. Also, while I was preparing my own laptop to set up a mini hardware and software development environment for deep learning, I met a lot of problems of installing drivers and frameworks. I'd like to share some of my experience here and hopefully it can save some time and be helpful for those who want to enter this field.
- uninstall Python3 from Mac
- built-in data types
- itertools
- collections
- heapq
- bisect
- functools
- show information
- create a job
- cancel a job
- datasets
- build a model
- checkpoints
- train a model
- evaluate a model
- inference using a model
- configs
- install Docker Engine on Ubuntu 20.04 LTS x64 docker install script. You may need to add your user to docker group to run docker without a root privilege.
- check status
- config
- remote
- branch
- commit
- git ignore
- ssh config for git
- Concepts
- Memory Hierarchy and Heterogeneous Programming
- Kernel
This repository is licensed under the MIT license.