If you want to show specific information on a Twitch profile, how do you do that? In this episode, Learn With Jason moderator Darrik Moberg (https://twitter.com/MDarrik) teaches us how to create custom extensions for Twitch that work on both the website and apps.
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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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.
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Due to schools & Colleges being closed due to Covid-19 Pandemic, the need for e-learning platforms like Byju’s has seen a rapid rise among school students. The rise is so fast that in just 3 months from the beginning of lockdowns its user base increased by twice.
Want to help school students learn with creative methods from an e-learning app?
WebClues Infotech is there for you to bring your vision to reality with its highly skilled e-learning App Development team. With past experience in developing different types of e-learning solutions like AshAce Papers, EduPlay Cloud, Squared, and many more, WebClues Infotech is fully equipped to develop an e-learning app development solution based on your need
Want more information on Byju’s like e-learning app Development?
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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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