Machine Learning · GitHub

The Swift machine learning library.

High Performance Chinese License Plate Recognition Framework.

Official Tensorflow implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (ICLR 2020)

A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility

• Focus on training speed.
• Speed comes for free with Tensorpack -- it uses TensorFlow in the efficient way with no extra overhead. On common CNNs, it runs training 1.2~5x faster than the equivalent Keras code. Your training can probably gets faster if written with Tensorpack.

:octocat: A curated awesome list of AI Startups in India & Machine Learning Interview Guide. Feel free to contribute!

Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

Code and data for "AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks" [TMLR 2024]

DeepQuantum for quantum computing

• AI-Enhanced Quantum Computing Framework: Seamlessly integrated with PyTorch, it utilizes technologies such as automatic differentiation, vectorized parallelism, and GPU acceleration for efficiency. It facilitates the easy construction of hybrid quantum-classical models, enabling end-to-end training and inference.
• User-Friendly API Design: The API is designed to be simple and intuitive, making it easy to initialize quantum neural networks and providing flexibility in data encoding.

Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications

Setup and customize deep learning environment in seconds.

Готовая база из 5 881 ответа по 21 тесту hh.ru - сдать с первой попытки

ETA: Extensible Toolkit for Analytics

Awesome-ComputerVision

A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.

One-click training of your own GPT. Training a GPT has never been easier for beginners. / 一键预训练+SFT一个属于自己的LLM，0基础训练GPT原来可以这么简单？

• 🔤 Flexible Tokenization — Character-level or custom tokenizers (Qwen, etc.)
• 🚄 Efficient Training — Multi-process acceleration & distributed training