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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 görüntüleme • 8 ay önce •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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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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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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Launching A2Z Sheet — for Open Source. 🎉 Live at Feats: > for the beginners / who struggle in open source > free > live progress tracking > download the notes > share (if you want) > doc for each module > video for each module
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Launching A2Z Sheet — for Open Source. 🎉 Live at Feats: > for the beginners / who struggle in open source > free > live progress tracking > download the notes > share (if you want) > doc for each module > video for each module

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>you will pay, endlessly >for foreign newcomers >this is normal >don't look back in anger
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