ML

机器学习

机器学习AI算法工程

AI之禅 机器之心 ATYUN订阅号 AI科技大本营的专栏 BestSDK 云+直播

平台

ML

NVIDIA(u2b, )

NVIDIA Developer(u2b, s, CUDA, doc, )

RE•WORK(u2b, )

MNN - 深度神经网络推理引擎(git, 书栈, )

Scientific Computing and Artificial Intelligence u

MIT OpenCourseWare(u, s, tw, ins, fb, AI, CS, math, )

brilliant(v, )

Theano(s, git, pypi, 书栈, )

XLearning(git, 文档, 书栈, )

Towards Data Science(s, )

天善智能学院(s, u, )

CityAge Media(u, )

SF Python u

Zfort Group(u, )

KDD2018 video u

Компьютерные науки计算机科学(u, )

Serrano.Academy u

臺大科學教育發展中心CASE u

机器学习知乎话题

中国人工智能学会 s CAAI wb

engineerknow mechanical coder u

台灣機器學習有限公司 u

Microsoft(s, research, u, )

MOPCON u

Vivian NTU MiuLab u

Cartesiam u

Stanford MLSys Seminars u

Center for Language and Speech (CLSP) @ JHU u

Stanford HAI u

Machine Learning at Berkeley u

The Alan Turing Institute u

Tübingen Machine Learning u

图宾根大学机器学习

MLSS Iceland 2014 u Machine Learning Summer School

Quora

Advances in AI(quora, )

Training Data for Machine Learning(quora, )

ABC of DataScience and ML(quora, )

Machine Learning: ML AI(quora, )

Python & Machine Learning(quora, )

HW accelerators eating AI(quora, )

Machine Learning(quora, )

Machine Learning 93(quora, )

Data science must needed(quora, )

Psychology of Machines(quora, )

Future TEC.(quora, )

Global AI Platform(quora, )

AMLD

AMLD指的是Applied Machine Learning Days（应用机器学习日），是一个面向机器学习和人工智能领域的国际会议，也是一个非营利性组织。该组织致力于促进机器学习和人工智能技术的应用和发展，并为学术界、工业界和政府机构提供交流和合作的平台。AMLD成立于2016年，总部位于瑞士日内瓦。该组织定期举办国际会议、研讨会和培训课程，吸引了来自全球各地的学者、研究人员、工程师、企业家和政府官员参加。

AMLD Africa u Applied Machine Learning Days u

北风网Python人工智能砖家王二狗

北风网Python人工智能-1-数学基础

北风网Python人工智能-2-Python基础

北风网Python人工智能-3-Python高级应用

北风网Python人工智能-4-机器学习

北风网Python人工智能-5-数据挖掘与项目实战

北风网Python人工智能-6-深度学习

北风网Python人工智能-7-自然语言处理

北风网Python人工智能-8-图像处理

北风网的大数据时代的Python金融应用实战

麦子人工智能视频教程砖家王二狗

麦子人工智能视频教程(第一阶段：Python数据分析与建模库)

麦子人工智能视频教程(第二阶段：机器学习经典算法)

麦子人工智能视频教程(第三阶段：机器学习案例实战)

DL

Carnegie Mellon University Deep Learning u

Deep Learning(quora, )

Supervisely u

Data Science

Ping Data Science(quora, )

Data Engineering Minds(quora, )

Data Sciences - Analytics(quora, )

Data Analytics or EnGines(quora, )

Data Science in Marketing(quora, )

UP主

ML up

迪哥有点愁 B, git bt 迪哥谈AI B 唐宇迪

sentdex(u2b, pythonprogramming, )

Data Application Lab u aipin

莫烦Python(u2b, )

将门-TechBeat技术社区(u2b, )

DeepMind(u2b, )

华校专(s, git, AI算法工程师手册, )

Knowing AI u2b B B更多

Dan Van Boxel(u2b, )

Pi School(u2b, )

Siraj Raval(u2b, )

Marc McLean(u2b, )

Sam Gu(u2b, )

Geoff Gordon(u2b, )

AiPhile u

Mark Jay(u2b, )

Arxiv Insights(u2b, )

AI壹号堂(B, )

yingshaoxo's lab(u2b, )

SuperGqq(s, )

Jeff Heaton(u, git, )

红色石头(s, ZH, 微信公众号/微博：AI有道)

Pascal Poupart(u, )

艾哈迈德·巴齐（Ahmad Bazzi)(u, )

Two Minute Papers(u, )

Kai博士(u, )

Daniel Bourke(u, )

Manisha Sirsat(quora, )

刘先生(u, )

Nicholas Renotte(u, )

DigitalSreeni u

Applied AI Course(u2b, )

CodeEmporium u

帅帅家的人工智障(B, )

李宏毅Hung-yi Lee(s, u, )

深度碎片(B, )

DeepPavlov u

啥都会一点的研究生(B, )

Math4AI(B, )

Acsic People(u, )

Pantech eLearning(u, )

爱可可-爱生活/Guang Chen/fly51fly/B u git

AI Prism(u, )

StatQuest with Josh Starmer(u, )

The Coding Train u

魏博士人工智能　抖音号： Dr.WeiAI

李文哲抖音号： vince88888

AI有啥用抖音号： 2016078732

AI技术资讯抖音号： JiuhuiLi2020

好玩的AI 抖音号： haowandeai

算法工坊抖音号： ALGHUB

阿里达摩院扫地僧抖音号： 54saodiseng

小乔斯在洛杉矶抖音号： Joyceni0610

MITCBMM u

FunInCode u B

王木头学科学 u B

硅谷吴军　抖音号： wujun001

The AI Epiphany u

技术喵　ｕ

珂学原理 u

高怡宣老師　ｕ

白手起家的百万富翁 u

William　ｕ

李政軒　ｕ

人工智能之趋势 u

Luis Serrano u

徐亦达 u

Art of the Problem u

Shusen Wang u en

Artificial Intelligence - All in One u

跨象乘云 u primo B

Weights & Biases u doc s v

AICamp u

卍卍子非鱼卍卍 B

Scc_hy git csdn

codebasics u

人工智慧與數位教育中心 NCCU AIEC u

解密遊俠 u

贪心学院 Greedy AI u

Min Yuan u

hashtag/machinelearningforbeginners

深度之眼官方账号 u

Learning AI u

財團法人人工智慧科技基金會 u

千锋教育 u

做大饼馅儿的韭菜 zh

机器学习-白板推导系列 shuhuai008 u B

容噗玩Data u

Justin Solomon u

WsCube Tech! ENGLISH u

Machine Learning with Phil u

就是不吃草的羊 B

Artificial Intelligence and Blockchain u

Colin Galen u

Si磕AI论文的女算法抖音号：49634887878

When Maths Meet Coding u

Artificial Intelligence Society u

Dr. Data Science u

Pista Academy u 波斯语

Parallel Computing and Scientific Machine Learning u

William u csdn

Machine Learning Street Talk u

Dr Alan D. Thompson u

Jeremy Howard u

Artem Kirsanov u

James Briggs u

論文導讀工gin師

TeachMe AI u

Priya Bhatia u

大白话AI u

Find Interesting AI u

Open Data Science u

炮老师AICG小课堂 u

arXiv

arXiv是由康奈尔大学运营的一个非营利性科学论坛，通常科学家在论文正式发表前会预先发到arXiv上防止自己的理论被剽窃．

DL up

飞桨Paddle(s, B, OCR(git), book, 文档, )

Deeplearning.ai(u2b, )

Sung Kim(u2b, )

Leonardo Zhou(u2b, )

Caffe2(书栈, )

deeplearningbook(s, Taro, )

DL4J(书栈, )

VisualDL(文档, 书栈, )

Alan Tessier u

Alexander Amini(u, )

deeplizard(u, )

Alex Smola(u2b, )

Lex Fridman(u2b, )

Alena Kruchkova(u2b, )

Alex(u2b, )

Rachel Thomas(u2b, )

Ian Goodfellow

Deep Sort(blog, )

Yannic Kilcher(u, git(v), )

Tien-Lung Sun u

茶米老師教室 u

AI转转转 u

fast.ai

git,

fastbook(git, 书栈, )

Jeremy Howard — The Story of fast.ai and Why Python Is Not the Future of ML Weights & Biases

Jeremy Howard: fast.ai Deep Learning Courses and Research | Lex Fridman Podcast #35

Data Science up

Data Professor(u, fb, medium, git, )

Data Science Conference(u, )

Data Science Courses(u2b, )

APMonitor.com u

Ken Jee u

小旭学长 u

Pepcoding u

Amulya's Academy u

Yoav Freund u

ai工具

火線班迪 u 阿石OMP u 肥拉超漫flcman u

框架

Long Liangqu

深度学习与PyTorch教程 Long Liangqu 网易云课堂

深度学习与TensorFlow 2入门实战 Long Liangqu 网易云课堂味道

深度学习与TensorFlow 2 Long Liangqu

magnet:?xt=urn:btih:F60CCA8F091866C1F6F35460882285386719588B&dn=%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E4%B8%8EPyTorch%E5%85%A5%E9%97%A8%E5%AE%9E%E6%88%98%E6%95%99%E7%A8%8B

2022 Version of Applications of Deep Neural Networks for TensorFlow and Keras (Washington University in St. Louis) Jeff Heaton

I built the same model with TensorFlow and PyTorch | Which Framework is better? Python Engineer

AI框架基础 ZOMI

Tensorflow

TensorFlow(site, install, pip, gpu, 教程, 指南, API, u, models, blog, medium, 书栈(1, 2, ), )

jikexueyuanwiki/tensorflow-zh TensorFlow官方文档中文版 s 过时

TensorFlow 2.x Insights EscVM

TensorFlow2.0 入门到进阶刘先生

【北京大学】人工智能 Tensorflow2.0 刘先生 bdy mocm

人工智能 Tensorflow 视频教程全集| 5 小时从入门到精通刘先生

TensorFlow Tutorial 修炼指南 Albert's Code Lab Creat Code Build

Tensorflow框架开发者学堂

TensorFlow快速入门与实战极客时间

TensorFlow 2项目进阶实战极客时间

Tensorflow Object Detection in 5 Hours with Python

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial freeCodeCamp 6:52:07 Tech With Tim

TensorFlow 2.0 Crash Course freeCodeCamp

机器学习从零到一　一　二　三　四　TensorFlow

TensorFlow Lite 视频系列教程　TensorFlow

深度学习应用开发-TensorFlow实践刘先生

TensorFlow 2.0 李政轩

TensorFlow Lite 视频系列教程 TensorFlow

TensorFlow 2 Beginner Course Python Engineer

Deep Learning for JavaScript Hackers | Use TensorFlow.js in the Browser Venelin Valkov

Made with TensorFlow.js TensorFlow

TensorFlow And Keras Tutorial | Deep Learning With TensorFlow & Keras | Deep Learning | Simplilearn

联想拯救者R9000P安装Ubuntu 21.04系统及运行TensorFlow1.X代码 csdn

Tensorflow CloseToAlgoTrading

Google's Machine Learning Virtual Community Day TensorFlow

TensorFlow Lite for Edge Devices - Tutorial freeCodeCamp

Android Apps TheCodingBug YOLOv4 TFLite Object Detection Android App Tutorial Using YOLOv4 Tiny, YOLOv4, and YOLOv4 Custom

[Tutorialsplanet.NET] Udemy - TensorFlow 2.0 Practical Advanced

深度学习框架Tensorflow2实战 DayDayUP 唐宇迪

Learn TensorFlow and Deep Learning (beginner friendly code-first introduction) Daniel Bourke

Learn TensorFlow and Deep Learning fundamentals with Python (code-first introduction) Part 1/2 Daniel Bourke 10:15:27

Learn TensorFlow and Deep Learning fundamentals with Python (code-first introduction) Part 2/2 Daniel Bourke 3:57:54

Aladdin Persson u

Deep Learning for Computer Vision with TensorFlow – Complete Course freeCodeCamp 1:13:16:40 colab

PyTorch

PyTorch u s doc tw fb medium PyTorch(github, u, s, 中文教程, )

pytorch/tutorials s the official PyTorch tutorials

PyTorch for Deep Learning & Machine Learning – Full Course freeCodeCamp 1:01:37:25

Getting Started With PyTorch (C++) Alan Tessier

Image Classification using CNN from Scratch in Pytorch AI-SPECIALS

Neural Network Programming - Deep Learning with PyTorch deeplizard

PyTorch - Python Deep Learning Neural Network API

Pytorch基础入门覃秉丰 git

PyTorchZeroToAll (in English) Sung Kim

PyTorch ClarityCoders

PyTorch for Deep Learning - Full Course / Tutorial freeCodeCamp 9:41:39

Deep Learning and Neural Networks with Python and Pytorch sentdex

TorchScript and PyTorch JIT | Deep Dive PyTorch

PyTorch and Monai for AI Healthcare Imaging - Python Machine Learning Course freeCodeCamp

PyTorch Tutorials - Complete Beginner Course Python Engineer

Introduction to PyTorch Tensors Coding Epocs

PyTorch - Deep Learning Course | Full Course | Session -1 | Python Tangoo Express

Getting Started With PyTorch (C++) Alan Tessier

PyTorch on Apple Silicon | Machine Learning Alex Ziskind

Invited Talk: PyTorch Distributed (DDP, RPC) - By Facebook Research Scientist Shen Li

7 PyTorch Tips You Should Know Edan Meyer

Learn PyTorch for deep learning in a day. Literally. Daniel Bourke 1:01:36:57

PyTorch Transfer Learning with a ResNet - Tutorial langfab

How to Install PyTorch GPU for Mac M1/M2 with Conda Jeff Heaton

Saving and Loading a PyTorch Neural Network (3.3) Jeff Heaton

I Built an A.I. Voice Assistant using PyTorch - part 1, Wake Word Detection The A.I. Hacker - Michael Phi

bentrevett/pytorch-seq2seq PyTorch Seq2Seq

PyTorch 深度學習快速入門教程（絕對通俗易懂）| 土堆教程我是土堆

Python机器学习算法与实战 Adam Sun zh

Python在机器学习中的应用 Adam Sun Daitu/Python-machine-learning

PyTorch深度学习入门和实战 Adam Sun

Machine Learning Course With Python Siddhardhan

Deep Learning With PyTorch - Full Course Python Engineer

PyTorch Beginner Series PyTorch

Pytorch Krish Naik

PyTorch Tutorials (2022) Mr. P Solver

Pytorch Krish Naik

PyTorch2.0 ZOMI

Pytorch+cpp/cuda extension 教學 tutorial AI葵

Aladdin Persson u

Install PyTorch for Windows GPU Jeff Heaton

Deep Learning with PyTorch: Zero to GANs freeCodeCamp

PyTorch Basics and Gradient Descent | Part 1 of 6

PyTorch Images and Logistic Regress | 2 of 6

Training Deep Neural Networks on GPUs | Part 3 of 6

Image Classification with Convolutional Neural Networks | Part 4 of 6 bk

Data Augmentation, Regularization, and ResNets | 5 of 6

Image Generation using GANs | Part 6 of 6

PyTorch: Zero to GANs Dhanabhon Subha-asavabhokhin

Deep Learning with PyTorch: Zero to GANs Jovian

Keras

Keras(s, git, Sequential, b, 文档(en, zh, ), )

Keras - Python Deep Learning Neural Network API deeplizard

Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial freeCodeCamp

Deep learning using keras in python DigitalSreeni

Deep Learning with Keras Krish Naik

Deep Learning with TensorFlow 2.0 and Keras

第一章神经网络基础以及TF2初探

第二章 TensorFlow 1.x and 2.x

第三章回归

第四章卷积神经网络

第五章更高级的卷积神经网络

第六章对抗生成网络

第七章 Word Embedding

第八章 RNN、Seq2Seq以及各种注意力机制

第九章 Auto-encoder 自编码器

第十章无监督学习（PCA，KMeans，RBM，DBN，VAE）

JAX

JAX The AI Epiphany

Intro to JAX: Accelerating Machine Learning research TensorFlow

JAX Course Weights & Biases

JAX Crash Course - Accelerating Machine Learning code! AssemblyAI

JAX Diffusers Community Sprint Talks: Day 1 HuggingFace

JAX Diffusers Community Sprint Talks: Day 2 HuggingFace

JAX Diffusers Community Sprint Talks: Day 3 HuggingFace

JAX talks HuggingFace

课程

Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science codebasics

机器学习算法地图 SIGAI

ML

Python AI Projects NeuralNine

No Black Box Machine Learning Course – Learn Without Libraries

freeCodeCamp Radu Mariescu-Istodor

AI 硬體選擇及模型的優化及部署人工智慧

AI 深度學習軟硬體及框架選擇經驗分享人工智慧

AppForAI 人工智慧開發工具 Windows 及 Linux 版操作介紹 (淡江大學資管系) 人工智慧

AppForAI-Windows 人工智慧開發工具 s

Machine Learning Explainability Workshop I Stanford Stanford Online

Machine Learning for Everybody – Full Course freeCodeCamp

【机器学习 | 理论与实战】编程 / Python（文刀出品）B git

Complete Machine Learning and Data Science Courses Nicholas Renotte

MIT 16.412J Cognitive Robotics, Spring 2016 MIT OpenCourseWare

ARTIFICIAL INTELLIGENCE Crack Concepts

跟著大師學科技 Meta School 元學院

Machine Learning freeCodeCamp

人工智能：模型与算法刘先生 drive 这个好

人工智能：模型与算法 - 浙江大学刘先生

人工智能：模型与算法中国大学MOOC-慕课

With The Authors Yannic Kilcher

Clustering and Segmentation Algorithms explained Unfold Data Science

Machine Learning Tutorial Python | Machine Learning For Beginners codebasics

AI Adventures Google Cloud Tech

Machine Learning Algorithm Binod Suman Academy

Neptune Integrations NeptuneAI

【機器學習 2023】(生成式 AI) Hung-yi Lee Autoregressive

【機器學習2022】Hung-yi Lee s git

【機器學習2021】(中文版) Hung-yi Lee

Next Step of Machine Learning (Hung-yi Lee, NTU, 2019) Hung-yi Lee

Advanced Topics in Deep Learning (Hung-yi Lee, NTU) Hung-yi Lee 2018

Machine Learning (Hung-yi Lee, NTU) Hung-yi Lee 2017

Machine Learning From Scratch In Python - Full Course With 12 Algorithms (5 HOURS) Python Engineer

Machine Learning from Scratch - Python Tutorials Python Engineer Patrick Loeber

Cognitive and AI IBM Technology

MIT 6.034 Artificial Intelligence, Fall 2010 MIT OpenCourseWare

MIT公开课6.034 人工智能1 (带字幕) 唐逸豪

Machine Learning || Part 1 Geek's Lesson

高级人工智能

邹博　机器学习　曹峰　BiteOfPython　Xuhui Lin　升级版第七期　bt:机器学习理论研究

机器学习课程(全）Min Yuan 2015

小象学院-机器学习班升级版III 砖家王二狗

Deep learning and machine learning HammerResources

Kaggle实战课程　小象　BiteOfPython

End-To-End Data Science with Kaggle | Competition speed run? Nicholas Renotte

Top Kaggle Solution for Fall 2022 Semester Jeff Heaton

七月在线邹博机器学期算法基础2015年　Min Yuan

大数据的统计基础(完)　掘金　BiteOfPython

课程-人工智能原理 People With_Guitar

北京大学__人工智能原理知识资源世界(KnowledgeWorld)

中科院高级人工智能全集(35：25：56)

CS188 Artificial Intelligence (Spring 2013) Prof. Pieter Abbeel

中国科学院大学高级人工智能沈华伟博弈(02：50：07)

人工智能导论浙江工业大学电子工程世界共80课时 12小时15分33秒

机器学习-浙江大学2021 刘先生

机器学习-浙江大学（研究生课程）刘先生 2017 可以搭配李航《统计学习方法》

Tensorflow for Deep Learning Research(Labhesh Patel, )

CS480/680 Intro to Machine Learning - Spring 2019 - University of Waterloo Pascal Poupart

Understanding Machine Learning - Shai Ben David | UWaterloo Rahul Madhavan

CS229: Machine Learning | Summer 2019 (Anand Avati) stanfordonline

Stanford CS229: Machine Learning

Stanford CS229 Machine Learning 2008 吴恩达（Andrew Ng）Stanford homemediaplayer2

机器学习（Machine Learning）吴恩达（Andrew Ng）la fe

吴恩达《2022新版机器学习》课程 NLP从入门到放弃 s

【斯坦福大学】深度学习（全192讲）吴恩达 iMuseums 27:19:55

Andrew Ng’s Machine Learning Specialization 2022 | What is it and is it worth taking? Thu Vu data analytics

EE104: Introduction to Machine Learning stanfordonline

DMQA Lab Open AI/ML Seminar 김성범[ 소장 / 인공지능공학연구소 ]

Meta Learning Shusen Wang

Meta Learning Siraj Raval

机器学习-45-ML-01-Meta Learning(元学习) csdn

Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019 stanfordonline

Machine Learning for Computational Fluid Dynamics Steve Brunton

CS230: Deep Learning | Autumn 2018 stanfordonline

CS545 - Information and Data Analytics Seminar Series(list, )

Data Analytics Crash Course: Teach Yourself in 30 Days freeCodeCamp

Machine Learning PyB TV NPTEL-NOC IITM Pantech eLearning

机器能像人一样思考吗？人工智能（一）机器学习和神经网络(李永乐老师)

人脸识别啥原理？人工智能（二）卷积神经网络(李永乐老师)

人工智能AI求职与技术(BitTiger官方频道 BitTiger Official Channel)

机器学习真人面试模拟

人工智能、大数据与复杂系统 JK

Machine Learning Coding Tech Daniel Bourke StatQuest with Josh Starmer

mathematicalmonk Shital Shah

Machine Learning & Deep Learning Fundamentals deeplizard

Deep Unsupervised Learning -- Berkeley Spring 2020 bilibili

(强推)李宏毅2021春机器学习课程啥都会一点的研究生帅帅家的人工智障

Machine Learning Theory Understanding Machine Learning - Shai Ben-David

CS547 - 人机交互研讨会系列斯坦福在线

AI, ML & Data Science - Training | Projects - Pantech E Learning Pantech eLearning

Artificial Intelligence: Knowledge Representation and Reasoning Artificial Intelligence Z S

July 2019 - Practical Machine Learning with Tensorflow IIT Bombay July 2018

An Introduction to AI - Mausam | IITD - NPTEL Rahul Madhavan

Statistical Learning - Rob and Trevor Hastie | Stanford Rahul Madhavan

Spring 2015: Statistical Machine Learning (10-702/36-702) Ryan T

Spring 2017: Statistical Machine Learning (10-702/36-702) Ryan T

ML - Yaser Abu-Mostafa | Caltech Rahul Madhavan

Machine Learning Course - CS 156 caltech

AI - Patrick Winston | MIT Rahul Madhavan

Computation and the Brain - Christos H. Papadimitriou December 26 - 28 2019 CSAChannel IISc

有趣的机器学习莫烦Python

机器学习算法基础覃秉丰 git

机器学习基础配套项目实战课程覃秉丰 git

机器学习系列课程 Lida Yan

机器学习（Machine Learning）吴恩达（Andrew Ng）la fe

Lecture Collection | Machine Learning 吴恩达（Andrew Ng）Stanford git

机器学习基础：案例研究（华盛顿大学）电子工程世界共116课时 8小时3分27秒

[2020] 统计机器学习 [Statistical Machine Learning]【生肉】图宾根机器学习 B

33:05:54Statistical Machine Learning — Ulrike von Luxburg, 2020 Tübingen Machine Learning

统计机器学习电子工程世界共41课时 1天47分24秒

统计机器学习(张志华) 刘先生

机器学习导论(张志华) 刘先生电子工程世界共42课时 1天4小时6分25秒

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寻找标准是关键

常见的聚类方法

原型聚类

亦称"基于原型的聚类"(prototype-based clustering)

假设:聚类结构能够通过一组原型刻画

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亦称"基于密度的聚类"(density-based clustering)

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KNN

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AI框架之计算图

"图计算"和"计算图"是不同的概念，尽管它们之间有一些关联。

"计算图"通常指的是一种表示计算过程的图形结构，其中节点表示计算操作，边缘表示数据流。它通常被用于深度学习中，以表示神经网络的计算过程。在计算图中，每个节点执行特定的数学运算，并将结果传递给后续节点。这种图形表示方式有助于优化计算和自动求导。

"图计算"是一种计算模型，它使用图形结构来表示和处理数据。它的基本思想是将数据存储为图形结构，然后使用图形算法来处理数据。图计算可以应用于许多领域，例如社交网络分析、推荐系统和生物信息学。

因此，尽管它们之间有一些相似之处，但"图计算"和"计算图"是不同的概念。"计算图"是一种表示计算过程的图形结构，而"图计算"是一种使用图形结构来表示和处理数据的计算模型。

RNN Recurrent Neural Networks

Recurrent Neural Networks - EXPLAINED! CodeEmporium

LSTM

LSTM Networks - EXPLAINED! CodeEmporium

蒙特卡洛 Monte Carlo

蒙特卡洛树搜索 Monte Carlo Tree Search (MCTS)

6. Monte Carlo Simulation MIT OpenCourseWare MIT 6.0002

蒙特卡洛树搜索基础（Monte Carlo Tree Search)　技术喵

【讀論文】蒙地卡羅詳細過程 | Monte Carlo Tree Search| 遊戲樹 K66

2 3 蒙特卡洛树搜索中国大学MOOC-慕课 s

Monte Carlo Tree Search 1 2 Udacity

Monte Carlo Tree Search (MCTS) Tutorial Fullstack Academy

蒙特卡洛 Monte Carlo Shusen Wang

Monte Carlo Inference 徐亦达

数学_蒙地卡罗法和Buffon needle简介 PengTitus

【数之道 21】随机抽样、蒙特卡洛模拟与逆转换方法 FunInCode

将简单的均匀分布抽样转化为复杂分布抽样的方法:

逆转换方法 Inverse Transform Sampling

【数之道 22】巧妙使用"接受-拒绝"方法，玩转复杂分布抽样 FunInCode

接受拒绝抽样 Acceptance Rejection Sampling

Monte Carlo simulation for Conditional VaR (Excel) NEDL

MATLAB小课堂——如何使用蒙特卡洛模拟进行预测？ MATLAB

Advanced 4. Monte Carlo Tree Search MIT OpenCourseWare

Tongkui Yu u

AI如何下棋？直观了解蒙特卡洛树搜索MCTS!!! 图灵鸡科技俱乐部

马尔可夫马尔科夫 Markov

OR 10-2 馬可夫性質與馬可夫鏈(李維OR) 小卒數理學堂

徐亦达机器学习课程 Markov Chain Monte Carlo (part 1) (part 2)(3)(4)徐亦达

Intro to Reinforcement Learning 强化学习纲要第二课马尔科夫决策过程上下 Bolei Zhou

15讲01 隐马尔科夫模型的基本概念　MM li

隐马尔科夫模型　Ouyang Ruofei

程序数学之随机过程 Jomy King

A friendly introduction to Bayes Theorem and Hidden Markov Models Serrano.Academy Luis Serrano

馬可夫不等式 CUSTCourses

Lecture 8: Markov Decision Processes (MDPs) CS188Spring2013

Finite Math: Introduction to Markov Chains Brandon Foltz

馬可夫鏈基礎1 Chen Kiwii 馬可夫鏈進階1 Chen Kiwii

Hidden Markov Model 徐亦达

程序数学之随机过程 Jomy King csdn

【数之道 20】5分钟理解'马尔可夫链'的遍历性与唯一稳态 Markov Chain's Ergodicity and Stationary Distribution FunInCode

Lecture 7: Markov Decision Processes - Value Iteration | Stanford CS221: AI (Autumn 2019) stanfordonline

Markov Decision Processes (MDPs) - Structuring a Reinforcement Learning Problem deeplizard

Stanford教授Daphne Koller 概率图模型 — 终极入门第三讲马尔可夫网络 (Markov Networks) pdf

Markov Chains Clearly Explained! Normalized Nerd

用Python介绍马尔可夫链！ Adrian Dolinay

[Tutorialsplanet.NET] Udemy - Unsupervised Machine Learning Hidden Markov Models in Python

MCMC, Markov Chain Monte Carlo

基于采样的马尔可夫链蒙特卡罗(Markov Chain Monte Carlo，简称MCMC)方法

[硬核公式推导系列] 蒙特卡洛模拟与MCMC 技术喵　 git

Box-Muller算法？

接受拒绝抽样 Acceptance Rejection Sampling

metropolis Hastings

Gibbs Sampling

为什么要使用MCMC方法？zh

一文读懂贝叶斯推理问题：MCMC方法和变分推断 zh

17讲02 近似推断法：MCMC和变分推断 MM li

随机数算法, Sobol列 v 25:07

维特比算法 The Viterbi Algorithm

基于维特比算法的文本分词 (Greedy Academy) 贪心学院 Greedy AI

任务024：分词维特比算法 William 砖家王二狗

Viterbi Algorithm Keith Chugg

汉语自然语言处理-维特比算法与NER-命名实体识别-viterbi algorithm-HMM-CRF-概率图模型-动态规划 Shurui Zhang B

Decoding Convolutional Codes: The Viterbi Algorithm Explained Iain Explains Signals, Systems, and Digital Comms

The Viterbi Algorithm : Natural Language Processing ritvikmath

(ML 14.11) Viterbi algorithm (part 1) mathematicalmonk

人工智慧 -- 機率統計法 (HMM 隱碼可夫模型與 viterbi 算法）陳鍾誠

STAT115 Chapter 14.6 Viterbi Algorithm Xiaole Shirley Liu

Digital Communications: Viterbi Algorithm UConn HKN

Hidden Markov Models 11: the Viterbi algorithm djp3

条件随机场 Conditional Random Fields

第8课条件随机场与应用七月在线 4399 gala

16讲02 条件随机场的定义与形式 MM li

任务260： CRF介绍砖家王二狗

任务390：利用CRF模型做命名实体识别 01 砖家王二狗

Conditional Random Fields - Stanford University (By Daphne Koller) Machine Learning TV

Conditional Random Fields Natalie Parde

Lec 9: Conditional Random Fields (1/3) (2/3) (3/3) LUCY Yin

因子分解机Factorization Machine, FM

直观讲解因子分解机Factorization Machine 技术喵

Steffen Rendle. Factorization machines pdf 2010 IEEE

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction arxiv 2017

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems arxiv 2018

Building a Social Network Content Recommendation Service Using Factorisation Machines - Conor Duke Python Ireland

最大熵

Maximum Entropy Methods Tutorial Complexity Explorer

Entropy (for data science) Clearly Explained!!! StatQuest with Josh Starmer

集成学习 Ensemble Learning

三个丑皮匠顶个诸葛亮

集成学习 Ouyang Ruofei

GradientBoost Ouyang Ruofei

[Tutorialsplanet.NET] Udemy - Ensemble Machine Learning in Python Random Forest, AdaBoost

Ensembles Luci Date

集成学习南京大学周志华教授亲讲 Darics

序列化方法 AdaBoost(Boosting家族) GradientBoost(XGBoost*) LPBoost

异质配准Alignment

并行化方法 Bagging Random Forest* Random Subspace

E = E' - A', diversity is A'

圣杯 "What is diversity" remains the holy grail problem of ensemble learning

How Do Random Forests Work & What is Ensemble Learning NeuralNine

多任务学习 Multi-task learning

Community Talks on Day 2 | PyTorch Developer Day 2021 PyTorch

Stanford CS330: Deep Multi-Task and Meta Learning stanfordonline

Stanford CS330: Deep Multi-Task & Meta Learning I Autumn 2021I Professor Chelsea Finn Stanford Online

Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022 Stanford Online

AutoML 机器学习自动化调参

机器学习自动化调参 Ouyang Ruofei

神经网络结构搜索 Neural Architecture Search Shusen Wang

神经网络（十二）自动神经网络（AutoML）与网络架构搜索（NAS）技术喵

Hyperparameter Tuning in Python with GridSearchCV NeuralNine

ROC Optimal Threshold ► Data Science Exercises #22 Gleb Mikhaylov

AutoML with Auto-Keras (14.1) Jeff Heaton

169 - Deep Learning made easy with AutoKeras DigitalSreeni

171 - AutoKeras for image classification using cifar10 data set DigitalSreeni

Automated Deep Learning with AutoKeras Data Heroes

I tried building a AUTO MACHINE LEARNING Web App 15 Minutes Nicholas Renotte

Neural Architecture Search Connor Shorten

Create Simple AutoML System from Scratch Jeff Heaton

机器学习可解释性

CVPR'20 Interpretable Machine Learning Tutorial Bolei Zhou

Talk | 微软亚洲研究院王希廷：基于逻辑规则推理的深度自可解释模型将门-TechBeat技术社区

对比学习 contrastive learning

对比学习（Contrastive Learning）是一种无监督学习方法，旨在通过将相似的样本进行比较来学习有用的表示。在对比学习中，算法试图将来自同一类别的样本分组在一起，并将来自不同类别的样本分开。这可以通过比较两个或多个样本的表示来实现，例如将它们映射到一个低维向量空间中。

对比学习通常用于解决许多计算机视觉问题，例如图像分类、目标检测和语义分割。在这些问题中，通常需要大量的有标签数据来训练模型，而对比学习则提供了一种可以使用无标签数据进行训练的替代方案。

在最近的研究中，对比学习已经被证明在许多任务上具有出色的性能，例如自然语言处理和推荐系统。由于其可扩展性和适应性，对比学习已经成为了当前深度学习领域的一个热门话题。

SimCLR sota

Talk | 剑桥大学在读博士生苏熠暄：对比搜索（Contrastive Search）—当前最优的文本生成算法将门-TechBeat技术社区

MoCo 论文逐段精读【论文精读】 Mu Li 视觉无监督表示学习动量对比学习

Momentum Contrast(MoCo)

对比学习论文综述【论文精读】 Mu Li

少样本学习 Few-Shot Learning Zero Shot One Shot

Meta Learning Shusen Wang

Meta Learning Siraj Raval

Meta-Learning and One-Shot Learning macheads101

Model Agnostic Meta Learning Siavash Khodadadeh

Learning to learn: An Introduction to Meta Learning Machine Learning TV

Meta learning by Hugo yet Shell

Ilya Sutskever: OpenAI Meta-Learning and Self-Play | MIT Artificial General Intelligence (AGI) Lex Fridman

各種奇葩的元學習 (Meta Learning) 用法 Hung-yi Lee

【機器學習2021】元學習 Meta Learning (一) - 元學習跟機器學習一樣也是三個步驟 Hung-yi Lee

【機器學習2021】元學習 Meta Learning (二) - 萬物皆可 Meta Hung-yi Lee

Few Shot Learning - EXPLAINED! CodeEmporium

Few-shot learning in production HuggingFace

OpenAI's CLIP for Zero Shot Image Classification James Briggs

Fast Zero Shot Object Detection with OpenAI CLIP James Briggs

Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022 Stanford Online

Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning The Global NLP Lab

注意力

神经网络（四）注意力机制技术喵

RNN模型与NLP应用 Shusen Wang

Transformer模型 Shusen Wang

【機器學習 2022】各式各樣神奇的自注意力機制 (Self-attention) 變型 Hung-yi Lee

Attention in Neural Networks CodeEmporium

损失函数

机器学习常用损失函数小结王桂波

机器学习如何选择回归损失函数的？ csdn

神经网络的损失函数为什么是非凸的? zh

联邦学习 Federated Learning

Chaoyang He u no瞎哔哔 B

联邦学习：技术角度的讲解（中文）Introduction to Federated Learning Shusen Wang

杨强 | 用户隐私，数据孤岛和联邦学习清华大学智能产业研究院

刘洋丨联邦学习的技术挑战和应用展望清华大学智能产业研究院

分布式机器学习 Shusen Wang

FedML联邦机器学习开源框架视频教程全集 Chaoyang He

[Tutorial] FedML: a research library for federated machine learning Chaoyang He

90秒入门联邦学习 Federated learning 微软智汇AI

什么是联邦学习（Federated Learning）？【知多少】 KnowingAI知智

详解联邦学习Federated Learning - 知乎机器朗读

联邦学习与个性化联邦学习感知互联与数据智能

AB测试 A/B testing

5 concepts of A/B testing you should know as a Data Scientist CodeEmporium

How to run A/B Tests as a Data Scientist! CodeEmporium

AB Testing概览课代表立正

A/B Testing：轻松Pass二轮面试！AB 测试具体步骤及参数详解，附具体案例演示及结论分析 Data Application Lab

A/B Testing面试干货：一个你以为你会但总挡住你拿offer的必学知识点 - A/B测试（第427期） Data Application Lab

商业分析师AB测试设计实战技巧，大厂Business Analyst为你实例解析AB Testing（第520期）Data Application Lab

AB test calculator (pet project) | Gleb Builds #2 Gleb Mikhaylov

CTC

Phoneme Detection with CNN-RNN-CTC Loss Function - Machine Learning Ali Yektaie

CTC for Offline Handwriting Recognition Oliver Nina

F18 Recitation 8: Connectionist Temporal Classification (CTC) u

S18 Lecture 14: Connectionist Temporal Classification (CTC) u

因果推断 Causal inference

什么是因果推断Causal inference？为什么数据科学家要知道这个？（第612期）

Data Application Lab

数据科学读书会 Book 17 – 因果推断因果效应(Causal Effect) Data Application Lab

数据科学读书会 Book 17 - 因果推断-因果推断的公式和模型 Data Application Lab

探索因果规律之因果推断基础（ft. The Book of Why by Judea Pearl) 技术喵

因果效应学习基础技术喵

《为什么》关于因果关系的新科学每天听书 Wise AudioBooks

蚁群算法

【数之道 04】解决最优路径问题的妙招-蚁群ACO算法 FunInCode

Autoencoder

What is an Autoencoder? | Two Minute Papers #86 Two Minute Papers

Simple Explanation of AutoEncoders WelcomeAIOverlords

Autoencoders - EXPLAINED CodeEmporium

What are Autoencoders? IBM Technology

Autoencoders - Ep. 10 (Deep Learning SIMPLIFIED) DeepLearning.TV

85a - What are Autoencoders and what are they used for? DigitalSreeni

Understanding and Applying Autoencoders in Python! Spencer Pao

85b - An introduction to autoencoders - in Python DigitalSreeni

Autoencoder Dimensionality Reduction Python TensorFlow / Keras #CodeItQuick Greg Hogg

Autoencoders Explained Easily Valerio Velardo - The Sound of AI

Autoencoders Made Simple! Professor Ryan

VAE Variational Autoencoder

Ali Ghodsi, Lec : Deep Learning, Variational Autoencoder, Oct 12 2017 [Lect 6.2]

Data Science Courses

Variational Autoencoders Arxiv Insights

Variational Autoencoders - EXPLAINED! CodeEmporium

Autoencoder Explained Siraj Raval

178 - An introduction to variational autoencoders (VAE) DigitalSreeni

179 - Variational autoencoders using keras on MNIST data DigitalSreeni

VAE-GAN Explained! Connor Shorten

What are Generative Models? | VAE & GAN | Intro to AI Zhuoyue Lyu

Build a Stable Diffusion VAE From Scratch using Pytorch freeCodeCamp

变分推断 Variational Inference

通常在研究贝叶斯模型中，需要去求解一个后验概率(Posterior)分布，但是由于求解过程的复杂性，因此很难根据贝叶斯理论求得后验概率分布的公式精确解，所以一种方法是用一个近似解来替代精确解，并使得近似解和精确解的差别不会特别大。一般求解近似解的方法有两种：第一种是基于随机采样的方法，比如用蒙特卡洛采样法去近似求解一个后验概率分布；第二种就是变分贝叶斯推断法。变分贝叶斯法是一类用于贝叶斯估计和机器学习领域中近似计算复杂积分的技术。它关注的是如何去求解一个近似后验概率分布。s

Variational Inference: Foundations and Modern Methods (NIPS 2016 tutorial)

Steven Van Vaerenbergh

如何简单易懂地理解变分推断(variational inference)？ zh

变分自编码

变分推断与变分自编码器 s

EM算法

The EM Algorithm Peter Green

其他

人工智慧在臺灣：產業轉型的契機與挑戰｜陳昇瑋研究員中央研究院Academia Sinica

BUILD and SELL your own A.I Model! $500 - $10,000/month (super simple!) Code with Ania Kubów

Machine Learning Projects You NEVER Knew Existed Nicholas Renotte

数据收集

Data Collection Project Ideas & Demos Tech With Tim

数据标注

ROC and AUC, Clearly Explained! StatQuest with Josh Starmer

145 - Confusion matrix, ROC and AUC in machine learning DigitalSreeni

实操揭秘数据标注项目的套路，有点得罪人了，阅后删月下跑项目

Image Annotation for Machine Learning Apeer_micro

label encoding, 把标签变成数字

数据增强

RubanSeven/Text-Image-Augmentation-python

数据不均衡 imbalanced data

149 - Working with imbalanced data for ML - Demonstrated using liver disease data DigitalSreeni

类别不平衡南京大学周志华 Darics

过采样, oversampling smote

欠采样, undersampling EasyEnsemble

阈值移动, threshold moving

数据可视化

Data Visualization with D3 – Full Course for Beginners [2022] freeCodeCamp

Data Visualization with D3.js - Full Tutorial Course(freeCodeCamp) 老版本

Tutorial: Real Time Data Visualization - d3, crossfilter, websockets in Python by Example

Other Level’s u

DataV Vue　git v

CNN Explainer s v

dair-ai/ml-visuals doc wx

链接: https://pan.baidu.com/s/1CC6BFfiw0DcyVfYTofmH9A 提取码: r4z3

Visualization and Interactive Dashboard in Python: My favorite Python Viz tools — HoloViz Sophia Yang

pyviz

Viz Sophia Yang

ContextLab/hypertools 用于获得对高维数据的几何洞察力的 Python 工具箱

Vispy s git

Matplotlib

How to Create a Beautiful Python Visualization Dashboard With Panel/Hvplot Thu Vu data analytics

szagoruyko/pytorchviz colab

Plotly

Data Visualization Using Python BOKEH | Python Bokeh Dashboard | Full Course Tangoo Express

Python数据可视化详解大全-从简单到完善到高级设置（Matplotlib/Seaborn/Plotly/常用统计图形）云开见明教育科技

Automatically Visualize Datasets with AutoViz in Python NeuralNine

EdrawMax v

Python Data Analysis Projects for 2022 | Data Analysis With Python | Python Training | Simplilearn

Build a Media Analysis Dashboard with Python & Cloudinary Patrick Loeber

Longer lessons storytelling with data

Data Visualisation Luci Date

Interactive Web Visualizations with Bokeh in Python NeuralNine

[Tutorialsplanet.NET] Udemy - 2022 Python Data Analysis & Visualization Masterclass

[Tutorialsplanet.NET] Udemy - The Complete Data Visualization Course 2020

Visualizing Binary Data with 7-Segment Displays Sebastian Lague

🔴 Visualizing Data Structures and Algorithms with VS Code Visual Studio Code

Data Visualization Tutorial Krish Naik using Qliksense

Data Visualisation Luci Date

D3 JS - Build Data Driven Visualizations with Javascript [svg animation, data engineering] Build Apps With Paulo

Plotnine: A Different Approach To Data Visualization in Python NeuralNine

7 Python Data Visualization Libraries in 15 minutes Rob Mulla

Machine Learning Course - Lesson 2: Visualizing Data with JavaScript Radu Mariescu-Istodor

Create Interactive Maps & Geospatial Data Visualizations With Python | Real Python Podcast #143 Real Python

Build a Chart using JavaScript (No Libraries) Radu Mariescu-Istodor

Machine Learning Model Evaluation in JavaScript Radu Mariescu-Istodor

Machine Learning Course Radu Mariescu-Istodor

Tableau

Tableau 是一个可视化分析平台，它改变了我们使用数据解决问题的方式，使个人和组织能够充分利用自己的数据。

Tableau in Two Minutes - Tableau Basics for Beginners Penguin Analytics

How to create Radial Chart in Tableau| Step-by-step Megha Narang

Tableau数据可视化，学完就掌握商业分析必备技能了！（第613期） Data Application Lab

Tableau零基础教程未明学院

Gourcer s u

a software version control visualization tool

电子教鞭

inux下netmeeting

红烛电子教鞭

deepin-draw

pointofix

部署 Deploy

How to Deploy Machine Learning Apps? Normalized Nerd

Kevin Goetsch | Deploying Machine Learning using sklearn pipelines PyData

Talk | 清华大学在读博士生胡展豪：可以骗过人工智能检测器的隐身衣将门-TechBeat技术社区

Deploy ML Models from Colab with FastAPI & ColabCode - Free ML as a Service 1littlecoder

Run Your Flask App In Google Colab | [ Updated Way ] Cyber Creed

How to run Google Colab or Kaggle notebooks on VSCODE (My experience running example code on GPU) convergeML

Deploying production ML models with TensorFlow Serving overview TensorFlow

Deployment of ML Models Krish Naik

Aladdin Persson u

Build & Deploy AI SaaS with Reoccurring Revenue (Next.js, OpenAI, Stripe, Tailwind, Vercel) freeCodeCamp

How to Deploy a Web App Using Multiple Methods (Azure, Render, MongoDB Atlas, Koyeb, and more ) freeCodeCamp

👍AI 项目本地部署-通用脚本🟢小白都能用 🟢 通吃GitHub上的AI相关项目🟢 Step by Step 一个脚本搞定 🟢 AI项目本地部署保成功 NiuGee AI

Deploy Python Applications - Google Cloud Run with Docker NeuralNine

Deploy Python Applications From Source - Google Cloud Run NeuralNine

Edge AI 開發板挑選完整指南 VisCircuit 電路筆記

TensorRT

TensorRT是英伟达（NVIDIA）推出的深度学习推理加速库，它针对深度学习模型的推理阶段进行了优化。TensorRT（TensorRT是Tensor Runtime的缩写）可以通过高度优化的网络层和推理算法，提供低延迟和高吞吐量的深度学习推理性能。

TensorRT的主要功能包括：

网络优化：TensorRT可以通过对模型进行层级优化、融合相邻层、剪枝和量化等技术，来提高模型的推理性能。它可以自动检测并融合相似操作，减少了内存带宽和计算需求。
精度校准：TensorRT支持对模型进行精度校准，从而在保持模型准确性的同时，进一步优化推理性能。它可以通过减少浮点运算的位数或者使用定点数表示来降低计算复杂度。
动态尺寸支持：TensorRT可以处理具有可变输入尺寸的模型。这意味着可以根据实际输入的尺寸动态调整网络的计算图和内存分配。
多平台和多框架支持：TensorRT可以与多个深度学习框架（如TensorFlow、PyTorch和Caffe）无缝集成，同时支持多个硬件平台（包括NVIDIA的GPU和DPU）。

使用TensorRT可以显著提高深度学习模型的推理速度和效率，特别适用于需要实时性能的应用场景，如自动驾驶、工业自动化、物体检测和视频分析等。

总之，TensorRT是一个优化深度学习推理的强大工具，它通过网络优化、精度校准和动态尺寸支持等功能，提供高性能的推理加速，从而加快了深度学习模型在实际应用中的部署和执行速度。

TensorRT更加偏向于深度学习模型的部署阶段。它专注于对已经训练好的模型进行优化和加速，以提高模型在推理阶段的性能和效率。

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