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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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TensorTonic

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44,023 views • 5 months ago •via X (Twitter)

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Our final module is out! We've covered the fundamentals of Information theory. topics include - > shannon entropy > KL divergence > information gain (decision trees) > mutual information > cross entropy loss > GANs training > Jensen-Shannon Divergence > Perplexity
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