In this video we learn about symmetric encryption in the command line using OpenSSL.
Open SSL is an all-around cryptography library that offers open-source application of the TLS protocol. First released in 1998, it is available for Linux, Windows, macOS, and BSD systems. OpenSSL allows users to perform various SSL related tasks, including CSR (Certificate Signing Request) and private keys generation and SSL certificate installation.
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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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This post looks at symmetric and asymmetric encryption and how this could be implemented in .NET Core. Symmetric encryption is fast and can encrypt or decrypt large amounts of text, streams or file
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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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