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Module for Graph Theory in Machine Learning is live! this includes fundamentals to - > computational graph (how pytorch works) > Graph representation > GNNs > Message Passing in GNNs > Spectral graph theory > random walks & markov chains

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TensorTonic

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22,040 просмотров • 8 месяцев назад •via X (Twitter)

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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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