Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"

Config files for my GitHub profile.

A tutorial series by Preferred.AI

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Sourcetrail - free and open-source interactive source explorer

Never use print for debugging again

Graph Neural Network Library for PyTorch

PRML algorithms implemented in Python

Deep Learning with PyTorch Quick Start Guide, published by Packt

Modin: Scale your Pandas workflows by changing a single line of code

Generate embeddings from large-scale graph-structured data.

A simple library for creating complex neural networks

Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

ThunderGBM: Fast GBDTs and Random Forests on GPUs

Swift for TensorFlow

Papers on Computational Advertising

A scalable machine learning library on Apache Spark

A distributed graph deep learning framework.

A natural language modeling framework based on PyTorch

A collection of important graph embedding, classification and representation learning papers with implementations.

Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI …

Returns latest research results by crawling arxiv papers and summarizing abstracts. Helps you stay afloat with so many new papers everyday.

This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'

1st Place Solution for CrowdFlower Product Search Results Relevance Competition on Kaggle.

A Python module for dealing with so called "typed bytes".

Ytk-learn is a distributed machine learning library which implements most of popular machine learning algorithms(GBDT, GBRT, Mixture Logistic Regression, Gradient Boosting Soft Tree, Factorization …

A curated list of network embedding techniques.

A library for efficient similarity search and clustering of dense vectors.