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we have released more blogs today on calculus fundamentals for machine learning and their interactive simulations. some of them includes - > backprop and gradient descents > local minima/saddle points > vector fields > taylor series > jacobian and hessian > partial derivatives

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TensorTonic

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66,825 次观看 • 6 个月前 •via X (Twitter)

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happy new year! we have released 10 more blogs today on math fundamentals and their interactive simulations. some of them includes - > markov chains > law of large numbers > bayes theorm > chevyshev's inequality > probability distributions > random variables > monte carlo
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happy new year! we have released 10 more blogs today on math fundamentals and their interactive simulations. some of them includes - > markov chains > law of large numbers > bayes theorm > chevyshev's inequality > probability distributions > random variables > monte carlo

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Most awaited Optimization techniques in Machine Learning module is out! We've covered the fundamentals of - > SGD variants > Convex vs Non-Convex > Momentum & Nesterov acc. > Adam, RMSprop > Loss landscapes > Regularization > Batch Norm > Newton's method & lagrange

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