lldreamvip Goto Github PK

115

Assistant for 115 to export download links to aria2-rpc

12306

IP封禁好厉害，希望大家都能顺利回家！！！

awesome-ai-cheatsheets

AI与数据科学各类工具库速查表与参考代码

bilibili_notes

攻城狮之家B站视频课程课件合集，2号仓库地址https://github.com/JokerJohn/bilibli_notes2.git

bilibli_notes2

攻城狮之家B站视频课程课件合集2，1号仓库https://github.com/JokerJohn/bilibili_notes.git

bilibli_notes2-1

攻城狮之家B站视频课程课件合集2，1号仓库https://github.com/JokerJohn/bilibili_notes.git

blog

:books: 计算机开放电子书归档

carnd-vehicle-detection

Vehicle Detection Project

chinese_from_dongxiexidian

mirror of dongxiexidian/Chinese

chinesenlpcorpus

搜集、整理、发布 中文 自然语言处理 语料/数据集，与 有志之士 共同 促进 中文 自然语言处理 的 发展。

classifier_multi_label

multi-label，classifier，text classification，多标签文本分类，文本分类，BERT，ALBERT，multi-label-classification

cnn-architectures

decentralized-smart-lock

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.

deepcreampy

Decensoring Hentai with Deep Neural Networks

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，近30万字。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

deoldify

A Deep Learning based project for colorizing and restoring old images

fate

An Industrial Level Federated Learning Framework

gitdemo

this is for bilibili demo

grokking_algorithms

Code for the book Grokking Algorithms (https://amzn.to/29rVyHf)

hello-world

just have a exercise

interpretablemlbook

《可解释的机器学习--黑盒模型可解释性理解指南》，该书为《Interpretable Machine Learning》中文版

javasdk

the Java SDK for hyperchain (developing)

leetcode_python

machine-learning-in-action-python3

《机器学习实战》python3源码

machine-learning-notes

My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (1000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(1000+页)和视频链接

magpie

Deep neural network framework for multi-label text classification

microservices

ml_model_deployment

model-eval-article-supplementary

Supplementary material for the article "Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning"