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Course content for the Freshman Track at LearnWeb3 - best for intermediates to start diving deeper into various categories of Web3 building
Repository housing all the content for the Senior Track at LearnWeb3
Dapp learning project for developers at all stages. Becoming and cultivating sovereign individuals. Nonprofit organization.
SnapHiC: Single Nucleus Analysis Pipeline for Hi-C Data
R package to combine SNP-level test statistics at various region levels
Repository of code for reproducing analysis and figures in manuscript regarding the flaw of using MAGMA with two-sided summary statistics and its implications on H-MAGMA.
Fear and volatility in crypto markets
Workshop on measuring, analyzing, and visualizing the 3D genome with Hi-C data.
Overview of the data QC, code, and GWAS summary output from the 2017 UK Biobank data release
《李宏毅深度学习教程》（李宏毅老师推荐👍，苹果书🍎），PDF下载地址：https://github.com/datawhalechina/leedl-tutorial/releases
Adding the Intel MKL to a Debian / Ubuntu system via one simple script
A NLTH Poker Agent using Counterfactual Regret Minimization
A lightweight command line tool for calculating poker hand probabilities
An open implementation of Pure CFR applied to ACPC poker games.
Implementations of CFR for solving a variety of Holdem-like poker games
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
Framework for Multi-Agent Deep Reinforcement Learning in Poker
A python implementation of Counterfactual Regret Minimization for poker
Awesome Game AI materials of Multi-Agent Reinforcement Learning
A RNN PokerBot implementing DeepStack strategies
Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
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
Python Data Science Handbook: full text in Jupyter Notebooks
apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.