In this post, we will be looking at the flaws of the traditional development environment and then GitHub Codespaces and the good stuff it has in store for developers. Without any further ado, let’s get started.
If you have project code hosted on GitHub, chances are you might be interested in checking some numbers and stats such as stars, commits and pull requests.
You might also want to compare some similar projects in terms of the above mentioned stats, for whatever reasons that interest you.
We have the right tool for you: the simple and easy-to-use little tool called GitHub Stats.
Let’s dive right in to what we can get out of it.
This interactive tool is really easy to use. Follow the three steps below and you’ll get what you want in real-time:
1. Head to the GitHub repo of the tool
2. Enter as many projects as you need to check on
3. Hit the Update button beside each metric
In this article we are going to compare three most popular machine learning projects for you.
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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.
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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.
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