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30-days-of-python

A 30 days of python programming challenge

albert

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

albert_zh

A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS, 海量中文预训练ALBERT模型

allennlp

An open-source NLP research library, built on PyTorch.

always-learning

404 Not Found的知识库：计算机理论基础、计算机技术基础、底层研究、安全技术、安全研究、人工智能、企业安全建设、安全发展、职业规划、综合素质、国内外优秀技术人

anomalydetection

Anomaly Detection Algorithms with Java

attention-ocr

Visual Attention based OCR

awesome-ai-ml-dl

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.

awesome-bert

bert nlp applications and resources

awesome-chinese-nlp

A curated list of resources for Chinese NLP 中文自然语言处理相关资料

awesome-python-cn

Python资源大全中文版，包括：Web框架、网络爬虫、模板引擎、数据库、数据可视化、图片处理等，由伯乐在线持续更新。

awesome-qa

😎 A curated list of the Question Answering (QA)

baidunetdiskplugin-macos

For macOS.百度网盘 破解SVIP、下载速度限制~

bert

TensorFlow code and pre-trained models for BERT

chatterbot

ChatterBot is a machine learning, conversational dialog engine for creating chat bots

chinese-bert-wwm

Pre-Training with Whole Word Masking for Chinese BERT

chinese-nlp-corpus

Collections of Chinese NLP corpus

chineseocr

yolo3+ocr

clean_code

代码简洁之道

cluepretrainedmodels

高质量中文预训练模型集合：最先进大模型、最快小模型、相似度专门模型

cnn-text-classification-tf

Convolutional Neural Network for Text Classification in Tensorflow

colab

common_algorithm

common algorithm

cpp_learning

C++_learning

cs224u

Code for Stanford CS224u

cs224u-project

Final project for CS 224U, Spring 2015

custom-object-detection

Custom Object Detection with TensorFlow

cv_pyimagesearch

cv_pyimagesearch

deep-learning-with-tensorflow-book

深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06