Implementation of learning rate finder in TensorFlow
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Updated
Mar 4, 2019 - Python
Implementation of learning rate finder in TensorFlow
A packages containing all popular Learning Rate Schedulers. Implemented in Keras Tensorflow
pyDCONTINPALS - A Python program for running the historical FORTRAN code CONTIN-PALS which solves Fredholm integral equations with convoluted exponential decays as kernels of the type that occur in the analysis of Positron Annihilation Lifetime Spectra (PALS).
pyLifetimeSpectraGenerator - A simple Python program for the generation of synthetic lifetime spectra consisting of discrete or distributed characteristic lifetimes.
pyTailFit - A simple Python program enabling tail-fitting for the analysis of lifetime spectra using least-square optimization.
30-year dental decay predictor using exponential modeling. Shows patients exactly when teeth need attention. Built in 4h 16m. Live demo available.
Tyre tread wear prediction using exponential decay. Predicts exact miles until replacement, stopping distance impact, MOT pass/fail. White-label ready.
Ring-buffer backed exponential decay reservoir
Motivation tracker for ADHD builders. Uses pharmacokinetic decay curves to predict project abandonment. Because motivation isn't infinite - it's exponential.
Smart caffeine response calculator with pharmacokinetic modeling. Track your buzz, predict your crash, optimize your intake. Mobile-first PWA.
Clinical trial patient retention simulator. Predicts dropout risk using behavioral decay modeling. Saves CROs millions in trial delays. Enterprise-ready.
Sample size calculation to exponentially decaying IGRA+ proportion
A three-dimensional taxonomy for intelligence systems using observable physical dimensions (Sound, Space, Time). Biomimetic framework emerging from production systems.
After reading the WEF report on jobs that can be affected by AI, I decided to simulate this demand with university courses to better allocate students to meet this demand.
🧪 Model patient retention in clinical trials, identify risks, and simulate interventions to reduce dropouts and save costs.
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