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TensorTonic

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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 görüntüleme

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 görüntüleme

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 görüntüleme

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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 görüntüleme • 10 ay önce

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 görüntüleme • 9 ay önce

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 görüntüleme • 9 ay önce

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 görüntüleme • 9 ay önce

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 görüntüleme • 9 ay önce

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 görüntüleme • 8 ay önce

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 görüntüleme • 9 ay önce

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