You can find all the source codes here: https://codepen.io/HunorMarton/pen/PoGbgqj
The images shown in the video are based or inspired by the work of various artists on Dribbble including:
Snowflake, Gingerbread figure, Start, Bear face by Claire Pinot: https://dribbble.com/shots/8678048-Advent-calendar
House by catalyst https://dribbble.com/shots/8702505-Winter-is-coming
Forest by Haley Harms https://dribbble.com/shots/14611450-Snow-Globe-Holiday-Graphic
Snowman by Elen Winata: https://dribbble.com/shots/14647070-Snow-Globe
Ribbon by Željka Živković https://dribbble.com/shots/12587762-Gifts
Stick by Shelby Warwood https://dribbble.com/shots/14485623-Holiday-Cookies-Illustration
Background patterns by https://www.svgbackgrounds.com/
Lights by Erdem https://dribbble.com/shots/9178510-Free-20-Christmas-Icons
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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