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In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
“连续八年成为全世界最受喜爱的语言，无 GC 也无需手动内存管理、极高的性能和安全性、过程/OO/函数式编程、优秀的包管理、JS 未来基石" — 工作之余的第二语言来试试 Rust 吧。本书拥有全面且深入的讲解、生动贴切的示例、德芙般丝滑的内容，这可能是目前最用心的 Rust 中文学习教程 / Book
Listing of papers about machine learning for proteins.
List of papers about Proteins Design using Deep Learning
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…
📖《网络代理与VPN应用详解》 详细阐述代理、隧道、VPN运作过程，并对GFW策略如：地址端口封锁、服务器缓存投毒、数字验证攻击、SSL连接阻断做相关的原理说明
Docker Turnkey for PHP/MongoDB service
My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(2000+页)和视频链接
A Systematic and Dynamic Pipeline for Single-Cell RNA Sequencing Analysis
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
A repository to host materials needed for workship in Multiple Instance Learning
Implementation of Protein Classification based on subcellular localization using ProtBert(Rostlab/prot_bert_bfd_localization) model from Hugging Face library, based on BERT model trained on large c…
A complete computer science study plan to become a software engineer.
Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis
Scripts (R, Python, Julia, etc.) used by RichardOnData
Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.
Notebooks accompanying the paper "Navigating the pitfalls of applying machine learning in genomics"
A toolkit to learn how to model and interpret regulatory sequence data using deep learning.
Analysis of epigenetic signals captured by fragmentation patterns of cell-free DNA
A curated list of the latest breakthroughs in AI (in 2021) by release date with a clear video explanation, link to a more in-depth article, and code.
SMN1 copy-number and sequence variant analysis from next generation sequencing data
RNAseq analysis notes from Ming Tang