機械学習 · GitHub

Unified Collective Communication Library

📚 OpenVINO™ の Jupyter Notebook チュートリアル

An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.

• LoRA LoRA is a parameter-efficient finetuning algorithm and is more efficient than full finetuning. Check out finetuning-lora for more details.
• FlashAttention LMFlow supports both FlashAttention-1 and the latest FlashAttention-2. Check out flash_attention for more details.

Python library to use and implement packages in OptunaHub

The registry of the OptunaHub packages

Real-time AI-assisted killer whale notification system (model and moderator portal) :star:

『ゼロから作る Deep Learning』(O'Reilly Japan, 2016)

『ゼロから作る Deep Learning ❷』(O'Reilly Japan, 2018)

Whatsmybitrate analyzes audio files for quality metrics such as bit rate, frequency, and codec type in bulk. It also generates spectrograms for visual representation of the audio spectrum. It supports a variety of audio formats, including MP3, FLAC, WAV, AAC, M4A, and more.

• Detailed Quality Analysis: Reports codec, sample rate, stated bit rate, and more.
• Perceptual Quality Estimation: Estimates the equivalent bitrate (e.g., 128, 192, 256, 320 kbps) based on spectral frequency cutoffs.

The AI Compendium, previously called the ML Compendium, curates resources and explanations of machine learning (ML) and deep learning, from a business problem through data, AI, engineering, and product.

Distributed PyTorch implementation of multi-headed graph convolutional neural networks

• Multi-headed Prediction for graph and node-level properties
• Distributed Training via DDP, FSDP (v1/v2), and DeepSpeed at supercomputing scale

Implementation of Prototypical Networks for Few Shot Learning (https://arxiv.org/abs/1703.05175) in Pytorch

Train your own neural networks to segment orthophotos.

Full-circle image roll estimation with calibrated abstention. Open weights, Apache-2.0. By ORTUS AI, the team behind CHEQIT.