Search Results for "python q learning" - Page 17
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667 projects for "python q learning" with 1 filter applied:
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AnyTrading
The most simple, flexible, and comprehensive OpenAI Gym trading
gym-anytrading is an OpenAI Gym-compatible environment designed for developing and testing reinforcement learning algorithms on trading strategies. It simulates trading environments for financial markets, including stocks and forex. -
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LLM Cookbook
LLM Introduction Tutorial for Developers, Chinese version
LLM Cookbook is an open-source learning repository designed to help developers understand how to build applications powered by large language models through practical examples and translated course material. The project adapts and reproduces content from widely known LLM developer courses and reorganizes it into a structured learning path tailored for developers who want to build real AI applications. It covers the essential topics required to start working with LLM APIs and frameworks,... -
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Detic
Code release for "Detecting Twenty-thousand Classes
Detic (“Detecting Twenty-thousand Classes using Image-level Supervision”) is a large-vocabulary object detector that scales beyond fully annotated datasets by leveraging image-level labels. It decouples localization from classification, training a strong box localizer on standard detection data while learning classifiers from weak supervision and large image-tag corpora. A shared region proposal backbone feeds a flexible classification head that can expand to tens of thousands of categories... -
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Promptify
se GPT or other prompt based models to get structured output
Promptify is an open-source Python library designed to simplify prompt engineering and the development of natural language processing pipelines using large language models. The project provides tools that help developers generate structured prompts for different NLP tasks and apply them across multiple generative AI systems. Instead of manually crafting prompts for each task, Promptify introduces a unified architecture that combines prompt templates, language model interfaces, and processing... -
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learn2learn
A PyTorch Library for Meta-learning Research
Learn2Learn is a PyTorch-based library focused on meta-learning and few-shot learning research. It provides reusable components and meta-learning algorithms, making it easier to build, train, and evaluate models that can quickly adapt to new tasks with minimal data. Learn2Learn is widely used in research for tasks such as few-shot classification, reinforcement learning, and optimization. -
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CORL
High-quality single-file implementations of SOTA Offline
CORL (Collection of Reinforcement Learning Environments for Control Tasks) is a modular and extensible set of high-quality reinforcement learning environments focused on continuous control and robotics. It aims to offer standardized environments suitable for benchmarking state-of-the-art RL algorithms in control tasks, including physics-based simulations and custom-designed scenarios. -
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Python Data Science Handbook
Python Data Science Handbook: full text in Jupyter Notebooks
The Python Data Science Handbook is a comprehensive collection of Jupyter notebooks written by Jake VanderPlas covering fundamental Python libraries for data science, including IPython, NumPy, Pandas, Matplotlib, Scikit-Learn and more. The project is designed for data scientists, researchers, and anyone transitioning into Python-based data work; it assumes you already know basic Python and focuses more on how to use the ecosystem effectively. -
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minimalRL-pytorch
Implementations of basic RL algorithms with minimal lines of codes
...The repository includes examples of widely used reinforcement learning methods such as REINFORCE, Deep Q-Networks, Proximal Policy Optimization, and Actor-Critic architectures. Most experiments are designed to run quickly using the CartPole environment so that users can focus on understanding algorithm logic rather than computational infrastructure. -
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iJEPA
Official codebase for I-JEPA
i-JEPA (Image Joint-Embedding Predictive Architecture) is a self-supervised learning framework that predicts missing high-level representations rather than reconstructing pixels. A context encoder sees visible regions of an image and predicts target embeddings for masked regions produced by a slowly updated target encoder, focusing learning on semantics instead of texture. This objective sidesteps generative pixel losses and avoids heavy negative sampling, producing features that transfer... -
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AB3DMOT
Official Python Implementation for "3D Multi-Object Tracking
AB3DMOT is a real-time 3D multi-object tracking framework designed for applications such as autonomous driving and robotics perception. The system processes detection results from 3D object detectors that analyze LiDAR point clouds and uses them to track multiple objects across consecutive frames. Its tracking pipeline relies on a combination of classical algorithms, including a Kalman filter for state estimation and the Hungarian algorithm for data association between detected objects and... -
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DeepMind Research
Implementations and code to accompany DeepMind publications
...The codebase is primarily Jupyter Notebooks and Python, reflecting an emphasis on experimentation and pedagogy rather than production packaging. -
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Project Based Learning
Curated list of project-based tutorials
project-based-learning is a community-curated open source repository that compiles programming tutorials focused on building real-world applications from scratch. It organizes resources by programming languages such as Python, Java, JavaScript, C++, Go, Rust, and many others. Each tutorial emphasizes practical, hands-on learning through project development rather than theoretical study. -
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picoGPT
An unnecessarily tiny implementation of GPT-2 in NumPy
picoGPT is a minimal implementation of the GPT-2 language model designed to demonstrate how transformer-based language models work at a conceptual level. The repository focuses on educational clarity rather than production performance, implementing the core components of the GPT architecture in a concise and readable way. It allows users to understand how tokenization, transformer layers, attention mechanisms, and autoregressive text generation operate in modern large language models. The... -
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MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
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ThinkJulia.jl
Port of the book Think Python to the Julia programming language
ThinkJulia.jl is an open source educational project that adapts Think Python by Allen B. Downey into the Julia programming language, with contributions by Ben Lauwens. It provides a comprehensive introduction to programming and computational thinking using Julia’s modern, high-performance features. The book is structured to gradually teach core concepts such as variables, control flow, functions, recursion, object-oriented programming, and data structures, while offering hands-on exercises... -
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ChatGPT Plugins Collection
An unofficial collection of Plugins for ChatGPT
ChatGPT-Plugins-Collection is a community-driven repository that gathers examples and resources for building, testing, and experimenting with ChatGPT plugins. The collection provides a variety of plugin implementations that showcase different use cases, helping developers learn how to extend ChatGPT’s functionality. It is designed to serve both as a learning resource for beginners and a reference point for more experienced developers. By centralizing community contributions, the repository... -
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LM Human Preferences
Code for the paper Fine-Tuning Language Models from Human Preferences
lm-human-preferences is the official OpenAI codebase that implements the method from the paper Fine-Tuning Language Models from Human Preferences. Its purpose is to show how to align language models with human judgments by training a reward model from human comparisons and then fine-tuning a policy model using that reward signal. The repository includes scripts to train the reward model (learning to rank or score pairs of outputs), and to fine-tune a policy (a language model) with... -
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PARL
A high-performance distributed training framework
PARL is a scalable reinforcement learning framework built on top of PaddlePaddle. It focuses on modularity and ease of use, supporting distributed training and a variety of RL algorithms. -
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OptiMate
Libraries for optimizing AI models, inference speed, and GPU usage
Optimate is an open source collection of libraries designed to optimize the performance and cost efficiency of artificial intelligence models across different stages of the machine learning lifecycle. It groups several internal optimization tools developed by Nebuly AI into a single repository that focuses on improving inference speed, reducing infrastructure usage, and streamlining model training workflows. Its modules help developers automatically apply optimization techniques that better... -
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PIFuHD
High-Resolution 3D Human Digitization from A Single Image
PIFuHD (Pixel-Aligned Implicit Function for 3D human reconstruction at high resolution) is a method and codebase to reconstruct high-fidelity 3D human meshes from a single image. It extends prior PIFu work by increasing resolution and detail, enabling fine geometry in cloth folds, hair, and subtle surface features. The method operates by learning an implicit occupancy / surface function conditioned on the image and camera projection; at inference time it queries dense points to reconstruct a... -
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GPT-NeoX
Implementation of model parallel autoregressive transformers on GPUs
This repository records EleutherAI's library for training large-scale language models on GPUs. Our current framework is based on NVIDIA's Megatron Language Model and has been augmented with techniques from DeepSpeed as well as some novel optimizations. We aim to make this repo a centralized and accessible place to gather techniques for training large-scale autoregressive language models, and accelerate research into large-scale training. For those looking for a TPU-centric codebase, we... -
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ArtLine
Deep learning tool that converts portrait photos into line art
ArtLine is a deep learning-based project focused on generating high-quality line art portraits from input images. It leverages neural network techniques built on top of the fastai library and PyTorch to transform photographic portraits into stylized line drawings. ArtLine is trained using datasets such as APDrawing and anime sketch colorization pairs to better understand facial structures and artistic line representation. An extended version integrates ControlNet, allowing users to guide the... -
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Reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI
Reinforcement-learning is a widely used educational repository that provides implementations, exercises, and solutions for a broad range of reinforcement learning algorithms, designed to complement foundational texts and courses in the field. The project collects popular approaches such as dynamic programming, Monte Carlo methods, temporal difference learning, Q-learning, SARSA, deep Q-networks, and policy gradient techniques, often demonstrated with Python and OpenAI Gym environments so users can experiment with agents learning in simulated tasks. ... -
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Alpa
Training and serving large-scale neural networks
Alpa is a system for training and serving large-scale neural networks. Scaling neural networks to hundreds of billions of parameters has enabled dramatic breakthroughs such as GPT-3, but training and serving these large-scale neural networks require complicated distributed system techniques. Alpa aims to automate large-scale distributed training and serving with just a few lines of code. -
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The Art of Programming
A collection of practical tips can be found at the bottom of this page
The Art of Programming (Second Edition) is a curated collection of programming problems and solutions originally derived from the Microsoft 100 Interview Questions blog series, later refined into a long-running tutorial and ultimately a published book. Created by July, the series began in 2010 and has since evolved into an in-depth exploration of algorithmic thinking, data structures, and coding interview preparation. The repository brings together 42 classic programming problems from the...
