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Starred repositories
List any node_modules 📦 dir in your system and how heavy they are. You can then select which ones you want to erase to free up space 🧹
The best open-source alternative to Superwhisper & Wispr Flow. Voice-to-text app for macOS with no subscription
A powerful tool that translates ComfyUI workflows into executable Python code.
A MIT-licensed, deployable starter kit for building and customizing your own version of AI town - a virtual town where AI characters live, chat and socialize.
This is a very simple AI adventure builder and runner.
Central Repository for the Epoch 0 coursework and quizzes. Contains all the content, cross-referenced and linked.
Roadmap to becoming an Artificial Intelligence Expert in 2022
A list of awesome beginners-friendly projects.
100+ Python challenging programming exercises
👩💻👨💻 Awesome cheatsheets for popular programming languages, frameworks and development tools. They include everything you should know in one single file.
EasyGA is a python package designed to provide an easy-to-use Genetic Algorithm. The package is designed to work right out of the box, while also allowing the user to customize features as they see…
Feature Selection using Genetic Algorithm (DEAP Framework)
General implementation of Advantage Actor Critic using Pytorch
multi-agent deep reinforcement learning for networked system control.
Building and training artificial neural networks (regression or classification) using the genetic algorithm.
Dear ImGui: Bloat-free Graphical User interface for C++ with minimal dependencies
Crack LeetCode, not only how, but also why.
An orchestration platform for the development, production, and observation of data assets.
Keras Implementation of popular Deep RL Algorithms (A3C, DDQN, DDPG, Dueling DDQN)
This repository contains most of pytorch implementation based classic deep reinforcement learning algorithms, including - DQN, DDQN, Dueling Network, DDPG, SAC, A2C, PPO, TRPO. (More algorithms are…
Author's PyTorch implementation of BCQ for continuous and discrete actions
Implementation of Efficient Off-policy Meta-learning via Probabilistic Context Variables (PEARL)