Popular GitHub Repositories for Codespaces Beta

A Collection of Variational Autoencoders (VAE) in PyTorch.

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

javascript gobang AI，JS五子棋AI，源码+教程，基于Alpha-Beta剪枝算法（不是神经网络）

Five lessons, learn how to really apply AI to your .NET Applications

[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)

Node.js + JavaScript reference client for the Realtime API (beta)

Experiments for understanding disentanglement in VAE latent representations

Explore machine learning and data science with Codespaces

A lightweight C++ machine learning library for embedded electronics and robotics.

Easy generative modeling in PyTorch

? LangChain for Swift. Optimized for iOS, macOS, watchOS (part) and visionOS.(beta)

alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, 2023, and 2024)

Pytorch implementation of FactorVAE proposed in Disentangling by Factorising(http://arxiv.org/abs/1802.05983)

? ERA Connect by VYNECT™ — The evolution of secure WhatsApp automation ERA Connect is part of the VYNECT™ ecosystem, offering a premium solution for safe, ethical WhatsApp automation and testing using direct WebSocket communication. Automate conversations, simulate interactions, and manage messages with maximum speed, security, and stability

A ?lightweight? frontend for Google's Gemini Pro.

?? A Codespaces template for getting up-and-running with LangChain in seconds!

Beta Machine Learning Toolkit

A simple WhatsApp bot that uses OpenAI's ChatGPT to respond to user inputs. Includes a cron to enable ChatGPT to send messages to recipients at periodic intervals.

Welcome to the Copilot + Codespaces Demo Experience!

Anomaly detection on the UC Berkeley milling data set using a disentangled-variational-autoencoder (beta-VAE). Replication of results as described in article "Self-Supervised Learning for Tool Wear Monitoring with a Disentangled-Variational-Autoencoder"