Open Source Projects

I have 16 projects on Github


🇦🇮 Deep Learning AI course on Coursera (Andrew Ng)

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One of the 'BEST' markdown preview extensions for Atom editor!

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Everything about Transfer Learning and Domain Adaptation--迁移学习

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VIP cheatsheets for Stanford's CS 229 Machine Learning

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Microsoft SEAL is an easy-to-use and powerful homomorphic encryption library.

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Paper list of multi-agent reinforcement learning (MARL)

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Jindong Wang's personal website

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:speak_no_evil: My Blog / Jekyll Themes

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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15个章节,近20万字。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 版权所有,违权必究 Tan 2018.06

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Deep Learning Specialization by Andrew Ng on Coursera.

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A curated set of resources for data science, machine learning, artificial intelligence (AI), data and text analytics, data visualization, big data, and more.

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CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.

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:mahjong: Chinese Copywriting Guidelines:中文文案排版指北(简体中文版)

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Deep Learning 101 with PaddlePaddle (『飞桨』深度学习框架入门教程)

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