1677566280
Learn Vim (the Smart Way) is a guide to learn the good parts of Vim.
There are many places to learn Vim: the vimtutor
is a great place to start and the help
manual has all the references you will ever need. However, the average user needs something more than vimtutor
and less than the help
manual. This guide attempts to bridge that gap by highlighting only the key features to learn the most useful parts of Vim in the least time possible.
This guide is written for both beginner and advanced Vimmers. It starts out with broad and simple concepts and ends with specific and advanced concepts. If you're an advanced user already, I would encourage you to read this guide from start to finish anyway, because you will learn something new!
Follow @learnvim for updates, Vim tips, etc.
This guide is and will always be free.
If you want to financially support this project, you can purchase this guide on Leanpub.
zh-CN
)es
)Author: iggredible
Source Code: https://github.com/iggredible/Learn-Vim
License: View license
1677566280
Learn Vim (the Smart Way) is a guide to learn the good parts of Vim.
There are many places to learn Vim: the vimtutor
is a great place to start and the help
manual has all the references you will ever need. However, the average user needs something more than vimtutor
and less than the help
manual. This guide attempts to bridge that gap by highlighting only the key features to learn the most useful parts of Vim in the least time possible.
This guide is written for both beginner and advanced Vimmers. It starts out with broad and simple concepts and ends with specific and advanced concepts. If you're an advanced user already, I would encourage you to read this guide from start to finish anyway, because you will learn something new!
Follow @learnvim for updates, Vim tips, etc.
This guide is and will always be free.
If you want to financially support this project, you can purchase this guide on Leanpub.
zh-CN
)es
)Author: iggredible
Source Code: https://github.com/iggredible/Learn-Vim
License: View license
1605176204
In this video, I’ll be showing you why I think it’s good to know Vim as a Developer.
#vim #vim editor #text editor #what is vim #speed,2x dev #vim for node.js
1598891580
Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.
The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.
#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources
1620898103
Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.
#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany
Visit Blog- https://www.xplace.com/article/8743
#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert
1617331066
Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.
Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.
This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.
The outline of this post include:
We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)
The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.
#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning