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TensorTonic

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

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How cool would it be if you could visualize everything inside an ML model

How cool would it be if you could visualize everything inside an ML model

25,187 просмотров

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

Part 2/30 of the LLM Series: RoPE (Rotary Position Embedding) How does a transformer know the difference between - "the dog bit the man" and "the man bit the dog"? The words are almost identical, but the meaning changes completely. RoPE encodes position as rotation, allowing transformers to understand relative order through geometry. Read more:

Part 2/30 of the LLM Series: RoPE (Rotary Position Embedding) How does a transformer know the difference between - "the dog bit the man" and "the man bit the dog"? The words are almost identical, but the meaning changes completely. RoPE encodes position as rotation, allowing transformers to understand relative order through geometry. Read more:

11,766 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 • 10 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

66,968 просмотров • 9 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

54,330 просмотров • 9 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.

48,430 просмотров • 9 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 • 9 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

35,683 просмотров • 8 месяцев назад

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

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

22,040 просмотров • 9 месяцев назад

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