Ascenoputing's repositories

CV-code-data-collection

colliection code & data & research on CV

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gluon-nlp

NLP made easy

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pyro

Deep universal probabilistic programming with Python and PyTorch

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rdkit

The official sources for the RDKit library

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automl

Google Brain AutoML

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Awesome-MXNet

A curated list of MXNet examples, tutorials and blogs.

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bruteforce-database

Bruteforce database

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darts

Differentiable architecture search for convolutional and recurrent networks

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Deep-Learning-Landscape

An (incomplete) overview of recent advances on Deep Learning Landscape

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DensePose

A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body

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fuzzdb

一个fuzzdb扩展库

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GitHubDaily

GitHubDaily 分享内容定期整理与分类。欢迎推荐、自荐项目,让更多人知道你的项目。

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HEB_CV_TOOLs

My CV tools

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LiteOS

code and manual

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mace

MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.

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NCRF

Cancer metastasis detection with neural conditional random field (NCRF)

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pypykatz

Mimikatz implementation in pure Python

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pyre-check

Performant type-checking for python.

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pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

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SecLists

SecLists is the security tester's companion. It's a collection of multiple types of lists used during security assessments, collected in one place. List types include usernames, passwords, URLs, sensitive data patterns, fuzzing payloads, web shells, and many more.

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SENet

Squeeze-and-Excitation Networks

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senet.pytorch

PyTorch implementation of SENet

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SMPyBandits

🔬 Research Framework for Single and Multi-Players 🎰 Multi-Arms Bandits (MAB) Algorithms, implementing all the state-of-the-art algorithms for single-player (UCB, KL-UCB, Thompson...) and multi-player (MusicalChair, MEGA, rhoRand, MCTop/RandTopM etc).. Available on PyPI: https://pypi.org/project/SMPyBandits/ and documentation on

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SSSS

"Deep Learning and Quantum Programming" Spring School @ Song Shan Lake

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ubelt

A Python utility belt. Extra batteries included!

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world-models

Reimplementation of World-Models (Ha and Schmidhuber 2018) in pytorch

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