Popular GitHub Repositories for Sequence Modeling

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

? Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX.

Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

Machine learning, in numpy

Bayesian Modeling and Probabilistic Programming in Python

Deep universal probabilistic programming with Python and PyTorch

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

[NeurIPS 2024 Best Paper][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!

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Leveraging BERT and c-TF-IDF to create easily interpretable topics.

Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

Uplift modeling and causal inference with machine learning algorithms

Probabilistic time series modeling in Python

StarVector is a foundation model for SVG generation that transforms vectorization into a code generation task. Using a vision-language modeling architecture, StarVector processes both visual and textual inputs to produce high-quality SVG code with remarkable precision.

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

OpenMMLab Pre-training Toolbox and Benchmark

LightSeq: A High Performance Library for Sequence Processing and Generation

OpenMMLab Self-Supervised Learning Toolbox and Benchmark

Top2Vec learns jointly embedded topic, document and word vectors.