Math In JavaScript Can Be Awesome
summary
TensorFlow.js core tensors run hardware accelerated in browser and Node including TFJS‑Node with native bindings and enable linear algebra workflows like Gram‑Schmidt and a custom two‑phase simplex implementation that converts a matrix problem into block matrices and solves it in the browser and server side showing orthonormal basis results. TensorFlow.js core tensors and BigInt Miller‑Rabin let code test 110‑digit and 2,530‑digit candidates with Miller‑Rabin probabilistic primality checks, and MathJax/KaTeX produces publication quality typeset math in the web UI.
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chapters (13)
- 0:00Start
- 0:16Background
- 0:42Deep Learning
- 3:36Tensorflow J'S
- 5:44Qr Decomposition
- 6:33Minimization Problem
- 7:46Use Existing Algorithm
- 9:06Simplex Method Implementation
- 12:12Orthonormal Basis
- 15:57Miller Raven Formality Test
- 20:02Typesetting
- 21:01Riemann Zeta-Function
- 22:11Summary
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