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Crash Course (sometimes stylized as CrashCourse) is an educational YouTube channel started by John Green and Hank Green (collectively the Green brothers), who became known on YouTube through their Vlogbrothers channel. [2] [3] [4] Crash Course was one of the hundred initial channels funded by YouTube's $100 million original channel initiative.
Margin-infused relaxed algorithm (MIRA) [1] is a machine learning algorithm, an online algorithm for multiclass classification problems. It is designed to learn a set of parameters (vector or matrix) by processing all the given training examples one-by-one and updating the parameters according to each training example, so that the current training example is classified correctly with a margin ...
In 2013, Animesh Nayan (another alumnus of IIIT Allahabad), who used to teach programming through his YouTube channel "mycodeschool", reached out to Harsha and discussed the idea of teaching computer science and programming to masses through videos and online practice platform.
In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning ...
3Blue1Brown videos are themed around visualizing math, including pure math such as number theory and topology as well as more applied topics in computer science and physics, The visuals are predominantly generated by Manim, a Python animation library written by Sanderson, though occasionally visuals are drawn from other software such as macOS's ...
23. Packers vs. Bills: We need to see Josh Allen do something absolutely ridiculous in the Super Bowl, like throw a defensive end into the stands or step on a linebacker’s chest or throw a bank ...
Q-learning is a model-free reinforcement learning algorithm that teaches an agent to assign values to each action it might take, conditioned on the agent being in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring ...
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