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A Simple Guide To The History Of Generative AI Bernard Marr

bernardmarr.com

Creates, rewrites or summarizes written content with language models.

Accelerate

github.com

A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision.

Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

bayeso

github.com

A simple, but essential Bayesian optimization package, written in Python.

Machine Learning made beautifully simple for everyone. Take your business to the next level with the Machine Learning platform.

Bokeh is a Python-based visualization library, capable of building plots from simple charts to interactive dashboards.

Simple yet flexible JavaScript charting library for the modern web.

Turn simple shots into photoshoot-quality images. Realistic lifestyle scenes, on-model photos, clean cutouts. Detail-first results.

Data Driven Code

github.com

Very simple implementation of neural networks for dummies in python without using any libraries, with detailed comments.

Deeplearn-rs provides simple networks that use matrix multiplication, addition, and ReLU under the MIT license.

emel

github.com

A simple and functional machine learning library written in Elixir.

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.

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.

knn-java-library

github.com

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

lambda-ml

github.com

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

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.

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

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.

Gitingest - Turn any Git repository into a simple text digest of its codebase so it can be fed into any LLM.