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TensorTonic

@TensorTonic • 6,546 subscribers

Run ML algorithms in cloud-native sandbox at https://t.co/1f7hGOZw21

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We’ve added interactive visualizations to all coding problems. You can now understand each coding problem more clearly and in depth before solving. Here’s an example using positional encodings from the transformer paper.

We’ve added interactive visualizations to all coding problems. You can now understand each coding problem more clearly and in depth before solving. Here’s an example using positional encodings from the transformer paper.

33,033 просмотров

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The math behind Machine Learning. You'll never forget the fundamentals after this. Coming soon.

The math behind Machine Learning. You'll never forget the fundamentals after this. Coming soon.

286,402 просмотров • 5 месяцев назад

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

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

66,812 просмотров • 5 месяцев назад

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

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

53,458 просмотров • 4 месяцев назад

We've released 10+ Linear Algebra concepts for Machine Learning! Here's one of the visualization how PCA identifies direction of maximum variance.

We've released 10+ Linear Algebra concepts for Machine Learning! Here's one of the visualization how PCA identifies direction of maximum variance.

48,290 просмотров • 5 месяцев назад

We've added 15 new Computer Vision problems. revise your concepts by coding them out: > max pooling > image rotation > gaussian blur kernel > sobel edge detection > image histogram > 2D convolution > IoU > Non-maximum supression etc

We've added 15 new Computer Vision problems. revise your concepts by coding them out: > max pooling > image rotation > gaussian blur kernel > sobel edge detection > image histogram > 2D convolution > IoU > Non-maximum supression etc

35,039 просмотров • 3 месяцев назад

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

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

44,023 просмотров • 5 месяцев назад

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