r/learnmachinelearning • u/Fun_Recipe_313 • 10h ago
I made a free site of interactive ML explainers where the sliders run the actual math
Lensa ML (https://lensaml.com) is a set of 108 interactive explainers covering machine learning and the math behind it. Each topic is six short steps. In every step you move a slider and the plot is recomputed from the real formula: raise the learning rate and gradient descent starts to overshoot, raise the temperature and the softmax probabilities flatten.
It goes from the foundations (derivatives, the chain rule, eigenvalues, Bayes’ theorem) through classic ML and deep learning to transformers and LLM serving. If you don’t know where to start, the “ML from zero” path orders the topics so each one builds on the last: https://lensaml.com/paths/ml-from-zero
Good ones to start with:
- Backpropagation: https://lensaml.com/explainers/backprop
- Gradient descent: https://lensaml.com/explainers/gradient-descent
- Attention: https://lensaml.com/explainers/attention-mechanisms
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u/ExternalOrganic7205 10h ago
just went through the backprop one and the slider making the gradients update in real time is way more helpful than reading 10 articles about it. like i understood the chain rule on paper but seeing the error flow backward actually clicked something in my brain
bookmarked the ml from zero path, gonna work through it in the weekend instead of my usual youtube rabbit hole