HackerNoon

AI Coding Tip 027 - Force Code Standards

Wire your standards into hooks, skills, and a judge, so the harness blocks violations before a human opens the diff.
marktechpost

A Coding Implementation to Build a Conditional Bayesian Hyperparameter Optimization Pipeline with Hyperopt, TPE, and Early Stopping

In this tutorial, we implement an advanced Bayesian hyperparameter optimization workflow using Hyperopt and the Tree-structured Parzen Estimator (TPE) algorithm. We construct a conditional search ...
marktechpost

A Complete End-to-End Coding Guide to MLflow Experiment Tracking, Hyperparameter Optimization, Model Evaluation, and Live Model Deployment

In this tutorial, we build a complete, production-grade ML experimentation and deployment workflow using MLflow. We start by launching a dedicated MLflow Tracking Server with a structured backend and ...
GitHub

ESCHR: A hyperparameter-randomized ensemble approach for robust clustering across diverse datasets

This example jupyter notebook on Google Colab provides a walkthrough of ESCHR analysis using an example scRNA-seq dataset. If you launch the notebook in Google Colab ...
Microsoft

AutoGen: Enabling next-generation large language model applications

“Capabilities like AutoGen are poised to fundamentally transform and extend what large language models are capable of. This is one of the most exciting developments I have seen in AI recently.” Doug ...
GitHub

CPA - Compositional Perturbation Autoencoder

CPA is a framework to learn the effects of perturbations at the single-cell level. CPA encodes and learns phenotypic drug responses across different cell types, doses, and combinations. CPA allows: ...