Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

[NeurIPS 2024 Best Paper Award][GPT beats diffusion🔥] [scaling laws in visual generation📈] Official impl. of "Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction". An *ultra-simple, user-friendly yet state-of-the-art* codebase for autoregressive image generation!

MiroThinker is a deep research agent optimized for complex research and prediction tasks. Our latest models, MiroThinker-1.7 and MiroThinker-H1, achieve 74.0 and 88.2 on the BrowseComp, respectively.

pySLAM is a hybrid Python/C++ Visual SLAM pipeline supporting monocular, stereo, and RGB-D cameras. It provides a broad set of modern local and global feature extractors, multiple loop-closure strategies, a volumetric reconstruction module, integrated depth-prediction models, and semantic segmentation capabilities for enhanced scene understanding.

This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the type of model.

Implementation of BERT that could load official pre-trained models for feature extraction and prediction

Qwen3-ASR is an open-source series of ASR models developed by the Qwen team at Alibaba Cloud, supporting stable multilingual speech/music/song recognition, language detection and timestamp prediction.

:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

The repo for "Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image" and "Metric3Dv2: A Versatile Monocular Geometric Foundation Model..."

Chai-1, SOTA model for biomolecular structure prediction

This repository contains the code to train and evaluate TRIBE v2, a multimodal model for brain response prediction

proof of concept for a transformer-based time series prediction model

[ICLR 2024] SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction

CTR prediction models based on deep learning(基于深度学习的广告推荐CTR预估模型)

MLGB is a library that includes many models of CTR Prediction & Recommender System by TensorFlow & PyTorch. 「妙计包」是一个包含50+点击率预估和推荐系统深度模型的、通过TensorFlow和PyTorch撰写的库。

CTR prediction model based on spark(LR, GBDT, DNN)

Code and models accompanying "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning"

Official implementation of "Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction"

Serverless Machine Learning Course for building AI-enabled Prediction Services from models and features

A fully open source biomolecular structure prediction model based on AlphaFold3

In this repository, I have developed the entire server-side principal architecture for real-time stock market prediction with Machine Learning. I have used Tensorflow.js for constructing ml model architecture, and Kafka for real-time data streaming and pipelining.

R package to compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and ML models. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference

[CVPR26] TESSERA is a foundation model that can process time-series satellite imagery for applications such as land classification and canopy height prediction. Developed at the University of Cambridge, it enables efficient extraction of temporal patterns from Earth observation

Linear Prediction Model with Automated Feature Engineering and Selection Capabilities

ProphitBet is a Machine Learning Soccer Bet prediction application. It analyzes the form of teams, computes match statistics and predicts the outcomes of a match using Advanced Machine Learning (ML) methods. The supported algorithms in this application are Neural Networks, Random Forests & Ensembl Models.

**Archived** Epic is a high performance statistical parser written in Scala, along with a framework for building complex structured prediction models.

pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks

A Python toolbox for conformal prediction research on deep learning models, using PyTorch.

Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction

Advanced Quantitative Factor Research: ML-powered stock return prediction with 72% performance improvement. Features comprehensive alpha factor library, systematic feature selection, and deep learning models (LSTM+ResNet achieving IC=0.06476).