Search results

emel

github.com

A simple and functional machine learning library written in Elixir.

A library for efficient similarity search and clustering of dense vectors.

Train custom concepts from input images with this simplified DreamBooth colab.

Open-source self-hostable end-to-end LLMOps platform unifying tracing, evals, simulations, datasets, gateway, and guardrails.

Datawrapper An open source data visualization platform helping everyone to create simple, correct and embeddable charts. Also at.

Simple and friendly way of teaching your non-data scientist/non-statistician colleagues. From Geckoboard's Data Literacy Lessons.

Hyperas

github.com

A very simple wrapper for convenient hyperparameter optimization.

Get your PDF forms and documents filled out quickly and accurately. Leverage AI to simplify your paperwork tasks.

Code for interactive simulacra of human behavior [[arxiv]](https://arxiv.org/abs/2304.03442).

Aims at simplifying the Data Science experience of deploying Kubeflow Pipelines workflows.

A beginner-friendly guide on using Keras to implement a simple Neural Network in Python.

Simple API for Neural Network. Better for image processing with CPU/GPU + Transfer Learning.

Just a simple implementation of K-Nearest Neighbors algorithm using with a bunch of similarity measures.

Simple, concise implementations of machine learning techniques and utilities in Clojure.

Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away.

LLMFlows

github.com

LLMFlows is a framework for building simple, explicit, and transparent LLM applications such as chatbots, question-answering systems, and agents.

Combine LLMs and code to create simple automations.

LLM-powered multiagent persona simulation for imagination enhancement and business insights.

A simple notebook demonstrating prompt-based music generation via Mubert API.

CEA-List's CAD framework for designing and simulating Deep Neural Network, and building full DNN-based applications on embedded platforms.

Creates or edits images with generative models and visual controls.

The simplest way to run an LLM locally. Supports tool calling and grammar constrained sampling.

A simple JavaScript library to help you quickly identify unseemly images; all in the client.

Simplifies and accelerates MLOps by bridging the gap between ML models and edge hardware.