Search Results for "prediction"
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Showing 559 open source projects for "prediction"
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Stock prediction deep neural learning
Predicting stock prices using a TensorFlow LSTM
...To identify patterns and trends in stock prices, deep learning techniques can be used for machine learning. Long short-term memory (LSTM) is a type of recurrent neural network (RNN) that is specifically designed for sequence modeling and prediction. -
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MiroFish
A Simple and Universal Swarm Intelligence Engine
MiroFish is a next-generation artificial intelligence prediction engine that leverages multi-agent technology and swarm-intelligence simulation to model, simulate, and forecast complex real-world scenarios. The system extracts “seed” information from sources such as breaking news, policy documents, and market signals to construct a high-fidelity digital parallel world populated by thousands of virtual agents with independent memory and behavior rules. -
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Chemprop
Message Passing Neural Networks for Molecule Property Prediction
Chemprop is a repository containing message-passing neural networks for molecular property prediction. -
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AIMr
The best AI Aimbot for Fortnite, Valorant, CS2, R6, COD, Apex, & more
...Written in Python, it uses cutting-edge AI technologies to ensure undetected, efficient aimbot functionality with customizable features. The software includes various aiming enhancements, such as recoil control, silent aim, and prediction capabilities, aimed at making gameplay smoother and more competitive. AIMr also provides visual customization options like field-of-view displays and detection indicators, allowing players to tailor their interface. The system is compatible with games that use human-shaped models, and although it functions effectively out of the box, optimizing it with CUDA-accelerated OpenCV is recommended for maximum performance. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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PMXT
A unified API for trading across prediction markets
PMXT is a unified API platform designed for accessing and trading across multiple prediction market exchanges through a single consistent interface. Inspired by CCXT for cryptocurrency exchanges, PMXT standardizes market data, trading operations, and event structures for platforms like Polymarket, Kalshi, Limitless, Smarkets, and more. The framework simplifies working with prediction markets by normalizing differences in APIs, formats, and conventions across providers. ... -
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NGBoost
Natural Gradient Boosting for Probabilistic Prediction
ngboost is a Python library that implements Natural Gradient Boosting, as described in "NGBoost: Natural Gradient Boosting for Probabilistic Prediction". It is built on top of Scikit-Learn and is designed to be scalable and modular with respect to the choice of proper scoring rule, distribution, and base learner. A didactic introduction to the methodology underlying NGBoost is available in this slide deck. -
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Alpamayo 1
Bridging Reasoning and Action Prediction
...Alpamayo supports tasks such as trajectory prediction, auto-labeling, and reasoning-based decision making. The system is optimized for high-performance GPU environments and is intended primarily for experimentation and benchmarking. Overall, it represents an advanced step toward integrating reasoning into autonomous driving pipelines. -
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CatBoost
High-performance library for gradient boosting on decision trees
...It is a machine learning method with plenty of applications, including ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. CatBoost offers superior performance over other GBDT libraries on many datasets, and has several superb features. It has best in class prediction speed, supports both numerical and categorical features, has a fast and scalable GPU version, and readily comes with visualization tools. CatBoost was developed by Yandex and is used in various areas including search, self-driving cars, personal assistance, weather prediction and more. -
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ConformalPrediction.jl
Predictive Uncertainty Quantification through Conformal Prediction
ConformalPrediction.jl is a package for Predictive Uncertainty Quantification (UQ) through Conformal Prediction (CP) in Julia. It is designed to work with supervised models trained in MLJ (Blaom et al. 2020). Conformal Prediction is easy-to-understand, easy-to-use and model-agnostic and it works under minimal distributional assumptions. Intuitively, CP works under the premise of turning heuristic notions of uncertainty into rigorous uncertainty estimates through repeated sampling or the use of dedicated calibration data. -
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Kalshi Trading Bot CLI
AI-native CLI for trading Kalshi prediction markets
Kalshi Trading Bot CLI is an AI-driven command-line tool designed to automate trading strategies on Kalshi prediction markets by combining quantitative modeling with real-time market data. It operates by conducting deep research on events, generating independent probability estimates, and comparing those estimates against current market prices to identify trading opportunities. The system incorporates advanced decision-making logic, including Kelly criterion-based position sizing and a structured multi-step risk evaluation process before executing trades. ... -
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PowerTrader_AI
Fully automated crypto trading powered by a custom price prediction AI
PowerTrader_AI is a fully open-source, automated cryptocurrency trading bot that combines a custom price prediction AI with a structured and tiered dollar-cost averaging (DCA) strategy to make real trading decisions on behalf of users. It continuously analyzes market data to forecast high and low price levels across multiple timeframes, using those predictions to determine when to open, scale into, or close positions automatically, which aims to take emotion out of trading and enforce discipline. ... -
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AI-Trader
100% Fully-Automated Agent-Native Trading
...The project integrates large language models, financial indicators, market analysis pipelines, and automated decision-making systems to support strategy generation and market prediction tasks. It is built to help researchers and developers experiment with AI-assisted trading strategies using historical and real-time financial data. The framework supports modular components for portfolio analysis, signal generation, market reasoning, and trading automation, allowing users to customize workflows for different trading styles and asset classes. ... -
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Open Aimbot
Universal Aim Assist Framework
Open Aimbot is an open-source aim assist framework created for Roblox and designed to provide a wide range of targeting and player-tracking features. The project offers a highly customizable interface that allows users to adjust targeting behavior, visual overlays, and activation settings. Built as a universal framework, it supports multiple exploit environments and device configurations. Open Aimbot includes numerous filtering options that can differentiate targets based on factors such as... -
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frugally-deep
A lightweight header-only library for using Keras (TensorFlow) models
...Avoids temporarily allocating (potentially large chunks of) additional RAM during convolutions (by not materializing the im2col input matrix). Utterly ignores even the most powerful GPU in your system and uses only one CPU core per prediction. Quite fast on one CPU core, and you can run multiple predictions in parallel, thus utilizing as many CPUs as you like to improve the overall prediction throughput of your application/pipeline. -
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Mozc
Mozc - a Japanese Input Method Editor designed for multi-platform
...The project originated as a subset of Google Japanese Input, released publicly under the BSD 3-Clause license for community use and development. Mozc offers core IME functionality such as text conversion, prediction, and dictionary-based input, enabling users to efficiently type and edit Japanese text. While Mozc shares much of its codebase with Google’s internal IME, it operates as an independent open source project without official support, guarantees, or stable release cycles. Developers can build Mozc from source for their preferred platform, and the repository includes detailed build instructions for Android, Linux, macOS, and Windows environments. -
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AlphaFold 3
AlphaFold 3 inference pipeline
AlphaFold 3, developed by Google DeepMind, is an advanced deep learning system for predicting biomolecular structures and interactions with exceptional accuracy. This repository provides the complete inference pipeline for running AlphaFold 3, though access to the model parameters is restricted and must be obtained directly from Google under specific terms of use. The system is designed for scientific research applications in structural biology, biochemistry, and bioinformatics, enabling... -
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DeepSeek-V3
Powerful AI language model (MoE) optimized for efficiency/performance
...It employs Multi-head Latent Attention (MLA) and the DeepSeekMoE architecture to enhance computational efficiency. The model introduces an auxiliary-loss-free load balancing strategy and a multi-token prediction training objective to boost performance. Trained on 14.8 trillion diverse, high-quality tokens, DeepSeek-V3 underwent supervised fine-tuning and reinforcement learning to fully realize its capabilities. Evaluations indicate that it outperforms other open-source models and rivals leading closed-source models, achieving this with a training duration of 55 days on 2,048 Nvidia H800 GPUs, costing approximately $5.58 million. -
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JiT
PyTorch implementation of JiT
JiT is an open-source PyTorch implementation of a state-of-the-art image diffusion model designed around a minimalist yet powerful architecture for pixel-level generative modeling, based on the paper Back to Basics: Let Denoising Generative Models Denoise. Rather than predicting noise, JiT models directly predict clean image data, which the research suggests aligns better with the manifold structure of natural images and leads to stronger generative performance at high resolution. This... -
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SAHI
A lightweight vision library for performing large object detection
A lightweight vision library for performing large-scale object detection & instance segmentation. Object detection and instance segmentation are by far the most important fields of applications in Computer Vision. However, detection of small objects and inference on large images are still major issues in practical usage. Here comes the SAHI to help developers overcome these real-world problems with many vision utilities. Detection of small objects and objects far away in the scene is a major... -
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ReservoirComputing.jl
Reservoir computing utilities for scientific machine learning (SciML)
ReservoirComputing.jl provides an efficient, modular and easy-to-use implementation of Reservoir Computing models such as Echo State Networks (ESNs). For information on using this package please refer to the stable documentation. Use the in-development documentation to take a look at not-yet-released features. -
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Protenix
A trainable PyTorch reproduction of AlphaFold 3
Protenix is an open-source, trainable PyTorch reimplementation of AlphaFold 3, developed by ByteDance with the goal of democratizing high-accuracy protein structure prediction for computational biology and drug-discovery research. Protenix provides a complete pipeline for turning protein sequences (with optional MSA / sequence alignment) or structural inputs (e.g. PDB/CIF) into full 3D atomic-level structure predictions. It supports both “full” models and lightweight variants such as “Protenix-Mini,” offering a trade-off between speed/compute cost and predictive accuracy — making structure prediction accessible even in resource-constrained environments. ... -
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TRIBE v2
A multimodal model for brain response prediction
TRIBE v2 is a multimodal foundation model developed by Meta AI for predicting human brain activity from naturalistic stimuli such as video, audio, and text. It is designed for in-silico neuroscience, enabling researchers to model how the brain responds to complex real-world inputs. The system integrates state-of-the-art encoders—including LLaMA for text, V-JEPA for video, and Wav2Vec-BERT for audio—into a unified Transformer architecture. This combined representation is mapped onto the... -
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AlphaGenome
Programmatic access to the AlphaGenome model
...The model analyzes DNA sequences of up to 1 million base pairs in length and can deliver predictions at single-base-pair resolution for most outputs. AlphaGenome achieves state-of-the-art performance across a range of genomic prediction benchmarks, including numerous diverse variant effect prediction tasks. -
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MiMo-V2-Flash
MiMo-V2-Flash: Efficient Reasoning, Coding, and Agentic Foundation
...The project positions the model for workflows that require tool use, multi-step planning, and higher throughput, rather than only single-turn chat. Architecturally, it highlights attention and prediction choices aimed at accelerating generation while preserving instruction-following quality in complex prompts. The repository typically serves as a launch point for running the model, understanding its intended use cases, and reproducing or extending its evaluation on reasoning and agent-style tasks. In short, MiMo-V2-Flash targets the “high-speed, high-competence” lane for modern LLM applications. -
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YuE
Open source AI model for generating full songs from lyrics prompts
...Unlike many shorter audio generators, the model is capable of producing songs that last several minutes while maintaining coherent musical structure and alignment with the provided lyrics. YuE introduces a family of models built on large language model architectures that process music generation as a sequence prediction task. YuE also incorporates techniques such as track-decoupled prediction and progressive conditioning to help manage complex audio signals and maintain consistency throughout long compositions. It includes inference scripts, prompt examples, evaluation tools, and training components that enable researchers and developers to experiment with AI-based music.
