ML
机器学习
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, ) | |
RE•WORK(u2b, ) | |
Scientific Computing and Artificial Intelligence u | |
Towards Data Science(s, ) | |
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 | |
engineerknow mechanical coder u | 台灣機器學習有限公司 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。 |
DL
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
sentdex(u2b, pythonprogramming, ) | |
莫烦Python(u2b, ) | |
将门-TechBeat技术社区(u2b, ) | DeepMind(u2b, ) |
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, ) |
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, ) |
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, ) | |
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 |
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 |
Artificial Intelligence - All in One u | |
AICamp u | 卍卍子非鱼卍卍 B |
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 |
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 |
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 |
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
Alan Tessier u | |
Alexander Amini(u, ) | deeplizard(u, ) |
Alex Smola(u2b, ) | Lex Fridman(u2b, ) |
Alena Kruchkova(u2b, ) | Alex(u2b, ) |
Rachel Thomas(u2b, ) | |
Deep Sort(blog, ) | |
茶米老師教室 u |
| fast.ai |
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git, |
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 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 |
框架
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
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 |
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 |
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 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 |
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 - 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 |
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 |
Machine Learning Explainability Workshop I Stanford Stanford Online |
Machine Learning for Everybody – Full Course freeCodeCamp |
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 |
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 |
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 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 |
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 |
【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) |
Machine Learning Coding Tech Daniel Bourke StatQuest with Josh Starmer |
Machine Learning & Deep Learning Fundamentals deeplizard |
Deep Unsupervised Learning -- Berkeley Spring 2020 bilibili |
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 |
机器学习系列课程 Lida Yan |
机器学习（Machine Learning）吴恩达（Andrew Ng）la fe |
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 |
应用数学基础(张志华)-北京大学 刘先生 |
人工智能 江西理工 罗会兰 电子工程世界 共40课时 8小时47分20秒 |
Python机器学习应用 电子工程世界 共27课时 3小时17分52秒 |
Apprentissage automatique - Université de Sherbrooke Hugo Larochelle |
Intelligence Artificielle - Université de Sherbrooke Hugo Larochelle |
DeepHack.Turing (2017) DeepPavlov |
Theoretical Deep Learning Course DeepPavlov |
python数据分析与机器学习实战 Yang Liu |
机器学习40讲 极客时间 |
Machine Learning with Python || Machine Learning for Beginners Geek's Lesson |
Machine Learning Course for Beginners freeCodeCamp |
机器学习 FunInCode |
数之道系列 FunInCode |
【臺大探索第26期】Future of AI：人工智慧大未來 臺大科學教育發展中心CASE |
人人可做的机器学习 跨象乘云 |
人工智能专业课程实验演示 跨象乘云 |
机器学习 生信宝典 wx |
程式設計 數老的肺炎教室 |
Machine Learning Elliot Waite |
AI And Machine Learning Full Course | AI Tutorial | Machine Learning Tutorial 2022 | Simplilearn 11:29:16 |
AI And Machine Learning Full Course 2022 | AI Tutorial | Machine Learning Tutorial | Simplilearn 9:59:10 |
Machine Intelligence Kimia Lab |
Machine Learning for beginners Learning AI |
Artificial Intelligence Lessons Dr. Daniel Soper |
人工智慧實驗教學課程 - 以高中職為例 |
How I'm Learning AI and Machine Learning macheads101 |
Machine Learning macheads101 |
Machine Learning Course - CS 156 caltech |
Miscellaneous but useful information about deep learning DigitalSreeni |
MACHINE LEARNING CSE & IT Tutorials 4u |
Complete Machine Learning playlist Krish Naik |
[Tutorialsplanet.NET] Udemy -Artificial Intelligence with Python |
[Tutorialsplanet.NET] Udemy - Machine Learning, Deep Learning and Bayesian Learning |
机器学习实战---电信客户流失问题 DayDayUP |
人工智能核心能力培养 DayDayUP |
Machine Learning Luci Date |
Stanford AA289 - Robotics and Autonomous Systems Seminar Stanford Online |
Learn Core Machine Learning for FREE | Ultimate Course for Beginners Ayush Singh |
B站首推！字节大佬花一周讲完的人工智能，2023公认最通俗易懂的【AI人工智能教程】小白也能信手拈来（人工智能|机器学习|深度学习|CV）等等随便白嫖！AI宝库 |
【全1024集】清华大佬关门弟子课程！这波不亏，全程高能，学完即可上岸，拿走不谢！Python大本营 |
这绝对是B站最全的了！AI人工智能、机器学习、深度学习、OpenCV、神经网络（附项目实战搭配学习）一口气全都学完！Python校长 61:06:06 |
【整整600集】北大教授196小时讲完的AI人工智能从入门到项目实战全套教程，全程干货无废话！学完变大佬！这还学不会我退出IT圈！机器学习/深度学习/神经网络 马士兵-人工智能学院 54:47:36 |
Setting Up CUDA, CUDNN, Keras, and TensorFlow on Windows 11 for GPU Deep Learning Jeff Heaton |
| 唐宇迪 |
|---|
【唐宇迪】机器学习600集！机器学习算法精讲及其案例应用，直接看时长！最全最完整的机器学习教程从零基础开始学习！-人工智能/深度学习/机器学习 迪哥谈AI 30:25:37 |
这也太全了！回归算法、聚类算法、决策树、随机森林、神经网络、贝叶斯算法、支持向量机等十大机器学习算法一口气学完！迪哥AI课堂 29:50:56 |
林轩田 Hsuan-Tien Lin(u, ) |
Machine Learning Foundations (機器學習基石) 机器学习基石 Hsuan-Tien Lin 电子工程世界 |
Machine Learning Techniques (機器學習技法) 机器学习技法 Hsuan-Tien Lin 电子工程世界 |
| 概率机器学习 Probabilistic Machine Learning |
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Probabilistic Machine Learning — Philipp Hennig, 2021 Tübingen Machine Learning [2020] 概率机器学习 [Probabilistic Machine Learning]【生肉】图宾根大学机器学习 B |
| 图机器学习 Machine Learning with Graphs |
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【图机器学习Machine Learning with Graphs】精译【Stanford 公开课 CS224W (Fall 2021)】(中英双语字幕) B 22:25:02 Stanford CS224W: Machine Learning with Graphs Stanford Online |
DL
2023 Spring 台大資訊 人工智慧導論 NTU CSIE FAI 陳縕儂 Vivian NTU MiuLab |
2022 Fall 台大資工 深度學習之應用 NTU CSIE ADL Vivian NTU MiuLab 2022 Fall 台大資訊 深度學習之應用 NTU CSIE ADL |
2022 Spring 台大資工 深度學習之應用 NTU CSIE ADL Vivian NTU MiuLab |
2021 Spring 台大資工 深度學習之應用 NTU CSIE ADL Vivian NTU MiuLab |
2020 Spring 台大資工 深度學習之應用 NTU CSIE ADL Vivian NTU MiuLab |
MIT 6.S192: Deep Learning for Art, Aesthetics, and Creativity Ali Jahanian MIT 6.S192：艺术、美学和创造力的深度学习 |
Deep Learning & Machine Learning KNOWLEDGE DOCTOR |
NVIDIA Deep Learning Course NVIDIA |
Deep learning conference Sure |
2022 人人有功練：資料科學深度學習 茶米老師教室 |
Deep Learning Binod Suman Academy |
Learn TensorFlow and Deep Learning (beginner friendly code-first introduction) Daniel Bourke |
Neural Networks from Scratch in Python（sentdex） |
PyTorch for Deep Learning - Full Course / Tutorial freeCodeCamp.org 9:41:39 |
MIT 6.S191: Introduction to Deep Learning Alexander Amini |
Deep Learning: A Crash Course ACMSIGGRAPH |
Deep Networks Are Kernel Machines (Paper Explained) Yannic Kilcher |
Bay Area Deep Learning School Shubhabrata Sengupta |
深度学习能工作的秘密 (Why Deep Learning Works)：深度神经网络中的隐式自正则化 |
深度学习理论（斯坦福）爱可可-爱生活 |
Deep Learning Theories Changkun Ou |
Deep Learning Tutorials Changkun Ou |
Deep Learning Applications Changkun Ou |
Deep Learning NPTEL-NOC IITM 迷途小书童 |
Theoretical Deep Learning Nilotpal Sinha Shital Shah |
Deep Learning for Computer Architects CoffeeBeforeArch |
DeepLearning - Mitesh Khapra, SKS Iyengar || IIT Ropar and Madras - NPTEL Rahul Madhavan |
Deep Learning - Andrew Ng Kian Katanforoosh | Stanford - OnlineHub Rahul Madhavan |
Deep Learning - Ali Ghodsi | STAT 946 - U.Waterloo Rahul Madhavan |
Deep Learning Course by Sargur N. Srihari CSAChannel IISc |
Deep Learning by Sargur N. Srihari CSAChannel IISc |
Deep Learning Course NVIDIA Developer |
Neural networks class - Université de Sherbrooke Hugo Larochelle |
[Coursera] Neural Networks for Machine Learning — Geoffrey Hinton Colin Reckons |
Deep Learning Crash Course for Beginners freeCodeCamp |
Practical Deep Learning for Coders - Full Course from fast.ai and Jeremy Howard freeCodeCamp |
Neural Networks from Scratch with Python and Opencv Pysource |
CS294-158 Deep Unsupervised Learning Dhanabhon Subha-asavabhokhin |
How Deep Neural Networks Work - Full Course for Beginners freeCodeCamp |
人工智能与机器学习 做大饼馅儿的韭菜 |
Intuitive Deep Learning 深度碎片 |
Deep Learning: CS 182 Spring 2021 RAIL |
NIPS 2016 Deep Learning for Action and Interaction Workshop RAIL |
Ilya Sutskever: Deep Learning | Lex Fridman Podcast #94 Lex Fridman |
GPT-4 Creator Ilya Sutskever Eye on AI |
Ilya Sutskever (OpenAI Chief Scientist) - Building AGI, Alignment, Spies, Microsoft, & Enlightenment Dwarkesh Patel |
Sam Altman回归！聊聊“叛变者”的恐惧与信念：OpenAI技术灵魂人物Ilya Sutskever 硅谷101 |
The Robot Brains Podcast u Ilya Sutskever |
伊利亚·苏茨克沃 苏神 OpenAI的联合创始人和首席科学家 谷歌大脑 人工智能科学家 AlphaGo论文作者之一 OpenAI到底是一家怎样的公司？ 量子位 Inside OpenAI [Entire Talk] Stanford eCorner |
OpenAI成长史：顶级资本与科技大佬的理想主义，冲突，抉择与权力斗争；马斯克、奥特曼、纳德拉与比尔·盖茨等人的背后故事【深度】 硅谷101 Greg Brockman OpenAI CEO CTO |
Deep Learning Basics: Introduction and Overview Lex Fridman |
MIT 6.S094, Lex Fridman schung168 |
MIT 6.S094 adriendod |
LEX Fridman MIT Lectures AR |
MIT 6.S094 Lily Z. |
Complete Deep Learning Krish Naik |
深度學習 Yen-Lung Tsai 1102政大【數學軟體應用】(深度學習) 課程 Yen-Lung Tsai |
Machine Learning & Neural Networks without Libraries – No Black Box Course freeCodeCamp |
动手学深度学习
DeepLearningAI
| Deep Learning Specialization |
|---|
(Course 1) Neural Networks and Deep Learning |
(Course 2) Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization |
(Course 3) Structuring Machine Learning Projects |
(Course 4) Convolutional Neural Networks |
(Course 5) Sequence Models |
Data Science
Python And Data Science Full Course | Data Science With Python Full Course In 12 Hours | Simplilearn |
Build 12 Data Science Apps with Python and Streamlit - Full Course(freeCodeCamp) |
数据挖掘 Dayin HE |
分布式项目实战 Online learning网络课堂 |
Data Science - Learn to code for beginners deeplizard |
Intro to Data Science Steve Brunton |
Python for Data Science NPTEL-NOC IITM |
Tools in Scientific Computing IIT Kharagpur July 2018 |
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 MIT OpenCourseWare |
Python for Data Science - Course for Beginners (Learn Python, Pandas, NumPy, Matplotlib) |
Data Analysis with Python - Full Course for Beginners (Numpy, Pandas, Matplotlib, Seaborn) freeCodeCamp 4:22:12 |
Data Analysis Tutorial for Beginners Geek's Lesson |
Data Analysis with Python for Excel Users - Full Course freeCodeCamp |
Build 12 Data Science Apps with Python and Streamlit - Full Course freeCodeCamp |
Data Analysis with Python Course - Numpy, Pandas, Data Visualization freeCodeCamp |
Intro to Data Science - Crash Course for Beginners freeCodeCamp |
Solving real world data science tasks with Python Pandas! Keith Galli |
Keynote Jake VanderPlas PyData |
Reproducible Data Analysis in Jupyter Jake Vanderplas |
Data Mining資料採礦課程 謝邦昌 |
Data Science 101 Data Professor |
Learn Data Science Tutorial - Full Course for Beginners freeCodeCamp |
Full Data Science Course Learn Python with Rune |
Data Science freeCodeCamp |
Data Science and Machine Learning with Python and R Krish Naik |
Polars: The Next Big Python Data Science Library... written in RUST? Rob Mulla |
Data Science Job Interview – Full Mock Interview freeCodeCamp |
python 数据分析（中国国家精品课程） 华人开放式课程MOOC |
Data Science/ML Projects JCharisTech |
数据清理
The Ultimate Guide to Data Cleaning towardsdatascience
算法
奥卡姆剃刀原理(ccam's razor) 严伯钧 v8:33 entities should not be multi-plied beyond necessity
LightGBM(site, paper, github, wiki, pypi)
Dijkstra's Algorithm(v, )
XGBoost 中文文档(书栈, ) |
XGBoost StatQuest with Josh Starmer |
算法：Xgboost提升算法 开发者学堂 |
XGBoost与LightGBM 数据科学家常用工具大PK——性能与结构 Data Application Lab |
Time Series Forecasting with XGBoost - Use python and machine learning to predict energy consumption Medallion Data Science |
极客学院机器学习训练营
scikit-learn (sklearn)
機器學習：使用Python (书栈, ) |
scikit-learn (sklearn) 0.21.3 官方文档中文版 (书栈, ) |
Scikit-Learn Python Tutorial | Machine Learning with Scikit-learn ProgrammingKnowledge |
Jake VanderPlas: Machine Learning with Scikit Learn PyData |
Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc) Keith Galli |
Learn Scikit Learn Normalized Nerd |
Professional Preprocessing with Pipelines in Python NeuralNine |
Precision & Recall in Machine Learning Explained NeuralNine |
机器学习Sklearn全套教程（程序员必备）千锋教育 drive |
Traditional Machine Learning in Python DigitalSreeni |
Scikit-Learn Tutorial | Machine Learning With Scikit-Learn | Sklearn | Python Tutorial | Simplilearn |
Scikit-Learn Course - Machine Learning in Python Tutorial freeCodeCamp |
Scikit-learn Crash Course - Machine Learning Library for Python freeCodeCamp |
Learning Scikit-Learn Google Cloud Tech |
Introduction to scikit-learn Lander Analytics |
Introduction to Python in Google Colab and Introduction to Sci Kit Learn Veronica Red |
Python in Data Science for Intermediate learndataa |
Understanding Pipeline in Machine Learning with Scikit-learn (sklearn pipeline) Dr. Data Science |
Machine learning in Python with scikit-learn Data School |
Scikit-Learn Model Pipeline Tutorial Greg Hogg |
Using Scikit-Learn Pipelines for Data Preprocessing with Python Nicholas Renotte |
预测
08预测 课堂商业
Stock Price Prediction & Forecasting with LSTM Neural Networks in Python Greg Hogg colab | Rain Prediction | Building Machine Learning Model for Rain Prediction using Kaggle Dataset SPOTLESS TECH | |
| Kaggle Titanic Survival Prediction | ||
|---|---|---|
Titanic Survival Prediction in Python - Machine Learning Project NeuralNine | Desafio Kaggle: Titanic - Preparando os dados - Parte 1 DevVerso [BR] | Logistic Regression with Python | Titanic Data | Your First Kaggle Project | Analytics Summit |
Kaggle Titanic Survival Prediction Competition Part 1/2 - Exploratory Data Analysis Jason Chong | ||
分类
CART - Classification And Regression Trees StatQuest with Josh Starmer |
算法：决策树 开发者学堂 |
Decision Tree Classification Clearly Explained! Normalized Nerd |
线性回归 & 线性模型 Linear Regression and Linear Models
Linear Regression and Linear Models StatQuest with Josh Starmer |
【千锋大数据】机器学习之线性回归教程（6集）千锋教育 |
多元线性回归, multi variate Linear Regression
18 多元线性回归 南京大学周志华 Darics |
逻辑回归 Logistic Regression
Logistic Regression StatQuest with Josh Starmer |
【千锋大数据】机器学习之逻辑回归教程（6集）千锋教育 |
線性機率模型 (LPM) 與邏輯斯迴歸 (Logistic Regression) 張翔老師 |
【Stata小课堂】第24讲：有序多分类Logistic回归(Ordinal Logistic Regression) Mingyu Zhang |
Big Data Analysis - Regression 李政軒 |
Tutorial 35- Logistic Regression Indepth Intuition- Part 1| Data Science Krish Naik |
08逻辑回归算法 课时46逻辑回归算法原理推导 互联网开发教程 tyd |
Logistic Regression - Is it Linear Regression? CodeEmporium |
决策树
Decision Tree Classification Algorithm in Telugu CSE & IT Tutorials 4u |
剪支 pruning |
随机森林 Random Forests
What is Random Forest? IBM Technology |
Random Forests StatQuest with Josh Starmer |
算法：随机森林与集成算法 开发者学堂 |
一套完整的基于随机森林的机器学习流程（特征选择、交叉验证、模型评估））生信宝典 |
Random Forest Algorithm Clearly Explained! Normalized Nerd |
How Do Random Forests Work & What is Ensemble Learning NeuralNine |
MLP
MLP-Mixer: An all-MLP Architecture for Vision arxiv towardsdatascience git git |
MLP-Mixer: An all-MLP Architecture for Vision (Machine Learning Research Paper Explained) Yannic Kilcher |
MLP Mixer Is All You Need? towardsdatascience morioh |
MLP-Mixer：一个比ViT更简洁的纯MLP架构 知乎 陀飞轮 |
MLP-Mixer: MLP is all you need... again? ... mchromiak |
Unofficial implementation of MLP-Mixer: An all-MLP Architecture for Vision pythonrepo |
Prediction using Artificial Neural Network (MLP) - Predict Car Price Roy Jafari |
What are MLPs (Multilayer Perceptrons)? IBM Technology |
Multilayer Perceptrons - Ep.6 (Deep Learning Fundamentals) Power H |
Perceptron Algorithm with Code Example - ML for beginners! Python Simplified |
聚类 Clustering 集簇
Theory of Clustering Understanding Machine Learning - Shai Ben-David |
人工智能案例：聚类实践 开发者学堂 |
ML: Clustering Data analysis with Python - Spring 2020 colab git |
Numpy: Kmeans Clustering from Scratch GNT Learning |
K-means & Image Segmentation - Computerphile |
K-Means Clustering From Scratch in Python (Mathematical) NeuralNine |
周志华 Darics |
聚类的"好坏"不存在绝对的标准 |
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 过程:先对原型初始化, 然后对原型进行迭代更新求解 代表:k均值聚类, 学习向量量化(LVQ), 高斯混合聚类 密度聚类 亦称"基于密度的聚类"(density-based clustering) 假设:聚类结构能够通过样本分布的紧密程度确定 过程:从样本密度的角度来考察样本之间的可连续性, 并基于可连接样本不断扩展聚类蔟 代表:DBSCAN, OPTICS, DENCLUDE 层次聚类(hierarchical clustering) 假设:能够产生不同粒度的聚类结果 过程:在不同层次对数据集进行划分, 从而形成树形的聚类结构 代表:AGNES(自低向上), DIANA(自顶向下) |
回归 Regression
算法：线性回归算法 开发者学堂 |
案例实战 信用卡欺诈检测 开发者学堂 |
线性回归 Ouyang Ruofei git |
How to implement Linear Regression from scratch with Python AssemblyAI |
高斯过程 v |
Lasso Regression Udacity |
Lecture 21: LASSO Anders Munk-Nielsen |
10b Machine Learning: LASSO Regression GeostatsGuy Lectures |
Polynomial Regression in Python NeuralNine |
Poisson regression with tidymodels for package vignette counts Julia Silge |
Regression Analysis | Full Course DATAtab |
How to do Multiple Linear Regression in Python| Jupyter Notebook|Sklearn Megha Narang |
Multivariable Linear Regression using Gradient Descent Algorithm in Python,Step by Step from scratch PAUL ACADEMY |
Multiple Linear Regression using python and sklearn Krish Naik |
Statistics PL15 - Multiple Linear Regression Brandon Foltz |
Linear Regression From Scratch in Python (Mathematical) NeuralNine |
简单线性回归简介（simple linear regression ）Python统计66——Python程序设计系列169 Andrew 程序设计 |
11 1 简单线性回归的统计描述 11 医学统计学-郝元涛（中山大学） |
Bayesian Linear Regression: Simple Linear Regression Review Lazy Programmer |
Bayesian Linear Regression: Distribution of Parameter Estimate Lazy Programmer |
Machine Learning Foundations Course – Regression Analysis freeCodeCamp |
Interpreting Linear Regression Results Sergio Garcia, PhD |
线性回归 Darics 南京大学周志华 |
KNN
【千锋大数据】3天快速入门机器学习（9集） 千锋教育
How kNN algorithm works Thales Sehn Körting
How to implement KNN from scratch with Python AssemblyAI
Heart Disease Predictor Model Using KNN Classifier |Machine Learning| Python | Project For Beginners AI Sciences
Implementation of KNN Algorithm using Iris Dataset in Jupyter Notebook | JAcademy
KNN Algorithm In Machine Learning | KNN Algorithm Using Python | K Nearest Neighbor | Simplilearn | KNN (K-Nearest Neighbor) Algorithm in Telugu CSE & IT Tutorials 4u | K - Nearest Neighbors - KNN Fun and Easy Machine Learning Augmented Startups |
Predicting CS:GO Round Winner with Machine Learning NeuralNine | K-Nearest Neighbors Classification From Scratch in Python (Mathematical) NeuralNine | K-Nearest Neighbors Algorithm From Scratch In Python The Teen Innovator |
时间序列 Time Series
算法：时间序列AIRMA模型 开发者学堂 |
案例：时间序列预测任务 开发者学堂 |
时间序列分析：用数据做预测（第595期）Data Application Lab |
数据科学读书会 Book 15 – 《Hands-on Time Series Analysis with Python》 时间序列分析 第一讲 Data Application Lab |
数据科学读书会 Book 15 - 时间序列分析 单变量时间序列v |
Structured Learning 4: Sequence Labeling Hung-yi Lee |
Time Series Prediction Siraj Raval |
Time Series Analysis：Data Scientist是如何做时间序列分析的？（第566期） |
Time Series Analysis (Forecasting, Mining, Transformation, Clustering, Classification) + Python code Hadi Fanaee git |
Data Mining資料採礦課程 謝邦昌 |
Time Series Analysis ritvikmath |
02417 Time Series Analysis Lasse Engbo Christiansen 2018 |
02417 Time Series Analysis, Fall 2017 Lasse Engbo Christiansen |
02417 Time series analysis, Fall 2016 Lasse Engbo Christiansen |
Time Series Theory Analytics University |
Time Series Forecasting Theory |
Multivariate Time Series Forecasting with LSTM using PyTorch and PyTorch Lightning (ML Tutorial) Venelin Valkov |
Multivariate Time Series Forecasting Using LSTM, GRU & 1d CNNs Greg Hogg |
1D Convolutional Neural Networks for Time Series Modeling - Nathan Janos, Jeff Roach PyData |
Convolutional neural networks with dynamic convolution for time series classification Krisztian Buza |
Webinar: Time-series Forecasting With Model Types: ARIMAX, FBProphet, LSTM NeptuneAI |
161 - An introduction to time series forecasting - Part 1 DigitalSreeni |
162 - An introduction to time series forecasting - Part 2 Exploring data using python DigitalSreeni |
163 - An introduction to time series forecasting - Part 3 Using ARIMA in python DigitalSreeni |
166 - An introduction to time series forecasting - Part 5 Using LSTM DigitalSreeni |
181 - Multivariate time series forecasting using LSTM DigitalSreeni |
Time Series Analysis (ARIMA) using Python Tathya Bislesan |
Time Series Analysis For Rainfall Prediction Using LSTM Model - Explained For Beginners AI Sciences |
Time Series Forecasting with XGBoost - Use python and machine learning to predict energy consumption Medallion Data Science |
Time Series Analysis with FB Prophet JCharisTech |
支持向量机 SVM
Support Vector Machines StatQuest with Josh Starmer |
算法：线性支持向量机 开发者学堂 |
【千锋大数据】机器学习之SVM教程（9集） 千锋教育 |
Understanding SVM ,its Type ,Applications and How to use with Python engineerknow |
Support Vector Machine Algorithm in Telugu CSE & IT Tutorials 4u |
Support Vector Machine - How Support Vector Machine Works | SVM In Machine Learning | Simplilearn |
Support Vector Machine - SVM - Classification Implementation for Beginners (using python) - Detailed Cloud and ML Online |
Support Vector Machine (SVM) Basic Intuition- Part 1| Machine Learning Krish Naik |
Kernel Method
Kernel Method 李政軒 |
神经网络, Neural Networks, NN
Neural Networks StatQuest with Josh Starmer |
Gradient Boost StatQuest with Josh Starmer |
Batch Normalization - EXPLAINED! CodeEmporium |
Optimizers - EXPLAINED! CodeEmporium |
Liquid Neural Networks MITCBMM |
Neural Networks from Scratch with Python and Opencv Pysource |
How Deep Neural Networks Work - Full Course for Beginners freeCodeCamp |
深度神经网络的工作原理 Brandon Rohrer |
Stanford Seminar - Incorporating Sample Efficient Monitoring into Learned Autonomy Stanford Online |
The Mathematics of Neural Networks Art of the Problem |
Illustrated Guide to Deep Learning The A.I. Hacker - Michael Phi |
How are memories stored in neural networks? | The Hopfield Network #SoME2 Layerwise Lectures |
Hopfield Networks is All You Need (Paper Explained) Yannic Kilcher |
Talk | FAIR研究科学家刘壮：高效和可扩展的视觉神经网络架构 将门-TechBeat技术社区 |
Neural Networks are Decision Trees (w/ Alexander Mattick) Yannic Kilcher |
Visualizing and Understanding Deep Neural Networks by Matt Zeiler Data Council |
GLOM: How to represent part-whole hierarchies in a neural network (Geoff Hinton's Paper Explained) Yannic Kilcher |
[DMQA Open seminar] Backbone Network in Deep learning Sejin Sim |
How to Create a Neural Network (and Train it to Identify Doodles) Sebastian Lague |
Neural Network Primer Luci Date |
10 Tips for Improving the Accuracy of your Machine Learning Models Jeff Heaton |
Neural Networks: Zero to Hero Andrej Karpathy OpenAI 核心成员, 特斯拉自动驾驶 |
你能不能训练一个GPT类大型语言模型？基地 安德鲁·卡帕西（Andrej Karpathy） |
Neural Network from Scratch | Mathematics & Python Code The Independent Code |
Gradient Descent From Scratch in Python - Visual Explanation NeuralNine |
Deriving the Ultimate Neural Network Architecture from Scratch #SoME3 Algorithmic Simplicity |
| 万能近似定理（universal approximation theorrm） | ||
|---|---|---|
神经网络的万能逼近定理已经发展到什么地步了？ zh | Why Neural Networks can learn almost anything Emergent Garden | |
RBF Networks
RBF Networks macheads101 |
Lecture 16 - Radial Basis Functions caltech |
Mod-01 Lec-27 RBF Neural Network nptelhrd |
Mod-01 Lec-28 RBF Neural Network (Contd.) nptelhrd |
参数追踪 参数可视化
Visualize Neural Networks
4 Ways To Visualize Neural Networks in Python JCharisTech | Track your machine learning experiments locally, with W&B Local - Chris Van Pelt Weights & Biases search | |
自动微分 自动求导
计算图
计算图 ZOMI 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 (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 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 |
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 |
【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 |
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
维特比算法 The Viterbi Algorithm
条件随机场 Conditional Random Fields
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
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） |
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] |
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 |
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) |
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 |
Other Level’s u |
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 |
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 |
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 |
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：
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。 |
NVIDIA TensorRT: High Performance Deep Learning Inference NVIDIA Developer |
扩散模型 Diffusion models
【AIGC】七千字通俗讲解Stable Diffusion | 稳定扩散模型 | CLIP | UNET | VAE | Dreambooth | LoRA 最佳拍档 |
Talk | 北京大学杨灵：扩散生成模型的方法、关联与应用 将门-TechBeat技术社区 |
Diffusion models explained in 4-difficulty levels AssemblyAI |
DDPM - Diffusion Models Beat GANs on Image Synthesis (Machine Learning Research Paper Explained) Yannic Kilcher |
Ultimate Guide to Diffusion Models | ML Coding Series | Denoising Diffusion Probabilistic Models The AI Epiphany |
Diffusion models The AI Epiphany |
Exploring the NEW Hugging Face Diffusers Package | Diffusion Models w/ Python Nicholas Renotte |
Stable Diffusion - What, Why, How? Edan Meyer 54:07 colab |
由浅入深了解Diffusion Model ewrfcas |
Creating Stable Diffusion Interpolation Videos sentdex |
midjourney v |
[ML News] Stable Diffusion Takes Over! (Open Source AI Art) Yannic Kilcher |
Stable Diffusion AI画图 LKs OFFICIAL CHANNEL s |
CompVis/stable-diffusion v Hugging Face |
Harmonai, Dance Diffusion and The Audio Generation Revolution Weights & Biases |
AI艺术 抖音号： 1764700788 askNK u |
Google's AI: Stable Diffusion On Steroids! 💪 Two Minute Papers |
30年前游戏角色画风一键升级！从粗糙像素风变成高清建模画风 量子位 |
Diffusion Models | Paper Explanation | Math Explained Outlier |
Diffusion models from scratch in PyTorch DeepFindr |
JEPA - A Path Towards Autonomous Machine Intelligence (Paper Explained) Yannic Kilcher |
Google's DreamFusion AI: Text to 3D sentdexGoogle's DreamFusion AI: Text to 3D sentdex |
I tried to build a REACT STABLE DIFFUSION App in 15 minutes Nicholas Renotte |
Stable Diffusion Is Getting Outrageously Good! 🤯 Two Minute Papers |
Stable Diffusion Version 2: Power To The People… For Free! Two Minute Papers |
[ML News] Multiplayer Stable Diffusion | OpenAI needs more funding | Text-to-Video models incoming Yannic Kilcher |
Google's Prompt-to-Prompt: Diffusion Image Editing sentdex |
Diffusion Model 수학이 포함된 tutorial 디퓨전영상올려야지 |
Stable Diffusion in Code (AI Image Generation) - Computerphile |
AI换脸，AI去马赛克是如何实现的？初识人工智能大火算法-扩散模型 基地 |
Diffusion and Score-Based Generative Models MITCBMM |
Generative Adversarial Networks (GANs) and Stable Diffusion TensorFlow |
Diffusion Models - Live Coding Tutorial dtransposed Diffusion Models - Live Coding Tutorial 2.0 dtransposed |
Kas Kuo Lab u |
MIT 6.S192 - Lecture 22: Diffusion Probabilistic Models, Jascha Sohl-Dickstein Ali Jahanian |
Diffusion Models for Inverse Problems Inference & Control Group Planning with Diffusion for Flexible Behavior Synthesis Inference & Control Group Hierarchically branched diffusion models Inference & Control Group Diffusion models as plug-and-play priors Inference & Control Group |
Tutorial on Denoising Diffusion-based Generative Modeling: Foundations and Applications Arash Vahdat |
【stable diffusion】由淺入深了解Diffusion擴散模型 HKCTO 唐宇迪 |
AI Art Taking World By Storm - Diffusion Models Overview deeplizard AI Art for Beginners - Stable Diffusion Crash Course deeplizard |
CS 198-126: Lecture 12 - Diffusion Models Machine Learning at Berkeley |
What are Diffusion Models? Ari Seff |
Talk | MIT许逸伦：解锁由物理启发的深度生成模型-从扩散模型到泊松流模型 将门-TechBeat技术社区 |
[專題解說] Introduction to Diffusion Model 擴散模型入門 [附程式碼] 教學 工gin師 |
號稱打敗 GAN 的生成模型: Diffusion Models TJWei |
| Stable Diffusion |
|---|
Stable Diffusion Online s |
CompVis/stable-diffusion |
AI Art with Stable Diffusion (Women of the World) deeplizard |
最火的AI作图模型，这5款免费下载，含提示词，配合 Stable-diffusion 来制作高清大图吧！ | 零度解说 |
Generating Realistic AI Images with Stable Diffusion NeuralNine |
为什么AI画画能既离谱又烧钱啊？？ 量子位 |
Stable Diffusion不用獨立顯卡，不需上網連線，10分鐘超簡單安裝教學就把AI繪圖搬回家，有NVIDIA獨顯繪畫更快，Stable Diffusion能單機使用，比Midjourney好用 老阿貝 |
Lesson 9: Deep Learning Foundations to Stable Diffusion, 2022 Jeremy Howard |
云端AI绘图软件+本地Stable Diffusion免安装版+懒人常用模型包,完全使用攻略-猩猩看了都会用的AI绘图视频教程 番茄市常听 |
AI For You u |
Easiest Way To Install Stable Diffusion & Generate AI Images NeuralNine |
教你用 Google colab 免費玩 Stable Diffusion 作出擬真美女圖片！ Lora、ControlNet 教學（iPhone、Android、筆電、Mac 均適用） 電腦王阿達 |
Stable Diffusion XL v |
JW608 Plays With Stable Diffusion! JW608 |
[Stable Diffusion AI畫圖插件] Composable LoRA加強版! 支援LoCon、LyCORIS，並能讓LoRA只在特定步數作用! 張宇帆 |
Stable Diffusion教學 使用Lora製作AI網紅 Kas Kuo Lab |
Stable Diffusion 教學 Kas Kuo Lab |
AI绘画】给美女们更换衣服 零度解说 |
Stable Diffusion Tutorials, Automatic1111 Web UI & Google Colab Guides, DreamBooth, Textual Inversion / Embedding, LoRA, AI Upscaling, Video to Anime SECourses |
Stable Diffusion Got Supercharged - For Free! Two Minute Papers |
生成扩散模型漫谈：条件控制生成结果 PaperWeekly 有参考文献 |
生成扩散模型漫谈（九）：条件控制生成结果 spaces |
生成扩散模型漫谈（十七）：构建ODE的一般步骤（下） spaces |
Mac上最好用的StableDiffusion客户端，Draw Things详细演示！The best local AI painting Stable DIffusion client Intro. 工具狂Toolbuddy |
Stable Diffusion 進階教學：Colab 如何套 Lora、動漫圖真人化、網拍模特不求人、黑白線稿自動上色 電腦王阿達 |
真人LORA训练全攻略！看这篇就够了 LORA模型 Stable diffusion 教程 真人模型 阿硕讲AI |
大白话AI | 图像生成模型之DDPM | 扩散模型 | 生成模型 | 概率扩散去噪生成模型 | Diffusion Model |
| MultiDiffusion |
|---|
pkuliyi2015/multidiffusion-upscaler-for-automatic1111 高清放大插件MultiDiffusion 小显存也能跑出4k图 低配福音 赛博法师 |
基础模型 Foundation Models Large Models
火遍全网的AI大模型，华为能搞出什么新花样？老石谈芯 |
AI大模型是什么？可以让人工智能和人类一样？GPT-3、M6大模型 啃芝士 |
#84 LAURA RUIS - Large language models are not zero-shot communicators [NEURIPS UNPLUGGED] Machine Learning Street Talk |
Talk | 微信AI高级研究员苏辉：微信AI大规模预训练语言模型WeLM 将门-TechBeat技术社区 |
Real World Applications of Large Models Weights & Biases |
Foundation models and the next era of AI Microsoft Research |
Emily M. Bender — Language Models and Linguistics Weights & Biases |
多模态 Multi-modal
多模态论文串讲·下【论文精读】 Mu Li |
CLIP 论文逐段精读【论文精读】 Mu Li |
CLIP 改进工作串讲（上）【论文精读】Mu Li |
CLIP 改进工作串讲（下）【论文精读】 Mu Li |
ViLT 论文精读【论文精读】 Mu Li |
ViT论文逐段精读【论文精读】 Mu Li mli/paper-reading |
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Paper Explained) Yannic Kilcher |
AI Hairball - ChatGPT + Stable Diffusion deeplizard |
Talk | 东京大学博士生刘海洋：多模态驱动谈话动作生成-质量与多样性 将门-TechBeat技术社区 |
OpenAI CLIP Explained | Multi-modal ML James Briggs |
Fast Zero Shot Object Detection with OpenAI CLIP James Briggs |
OpenAI's CLIP for Zero Shot Image Classification James Briggs |
Fast intro to multi-modal ML with OpenAI's CLIP James Briggs |
OpenAI CLIP: ConnectingText and Images (Paper Explained) Yannic Kilcher |
Domain-Specific Multi-Modal Machine Learning with CLIP Pinecone |
CLIP: Connecting Text and Images Connor Shorten |
OpenAI CLIP - Connecting Text and Images | Paper Explained Aleksa Gordić - The AI Epiphany |
OpenAI’s CLIP explained! | Examples, links to code and pretrained model AI Coffee Break with Letitia |
Talk | 微软高级研究员杨征元：统一的视觉语言模型 将门-TechBeat技术社区 |
Vision Transformer (ViT) 用于图片分类 Shusen Wang |
Vision Transformers (ViT) Explained + Fine-tuning in Python James Briggs |
ImageBind Meta AI
只有Meta才懂多模态，ImageBind，在一个嵌入的空间中补齐六种模态。像人一样，感受完整的空间。突破语言的桎梏，将关注度重新吸引回元宇宙。 老范讲故事 |
【分享】LLM论文研读 | ImageBind One Embedding Space To Bind Them All | 六种模态大统一 | Kevin分享 | Meta AI 最佳拍档 |
facebookresearch/ImageBind |
ImageBind: a new way to ‘link’ AI across the senses meta |
AI Safety
AI Safety Times Infinity |
【機器學習2022】自然語言處理上的對抗式攻擊 (由姜成翰助教講授) Hung-yi Lee 1 2 |
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 |
Talk | 几何的魅力: 黑盒攻击新策略 将门-TechBeat技术社区 |
ML会议
Steven Van Vaerenbergh u |
CVPR
NIPS
ICLR
ICML
ACML
NeurIPS
MLSP
CompSci 188
CompSci 188 Shital Shah |
谷歌学术标签
Book
复旦大学邱锡鹏教授的《神经网络与深度学习》 人工智能学习室 19:05:43 |
机器学习实战（Machine Learning in Action） (书栈, ) |
Interpretable Machine Learning (书栈, ) |
ML Kit 中文文档 (书栈, ) |
spark机器学习算法研究和源码分析 (书栈, ) |
ml5.js - Machine Learning for Web (书栈, ) |
机器学习训练秘籍（Machine Learning Yearning 中文版） (书栈, ) |
Pipcook v1.0 机器学习工具使用教程 (书栈, ) |
花书 deeplearningbook(s, ) |
awesome-material git |
foochane/books git |
lovingers/ML_Books git 差评 |
深度学习入门-基于Python的理论与实现 deep-learning-from-scratch git |
周志华 机器学习 西瓜书
【一起啃书】机器学习西瓜书白话解读 致敬大神 13:10:47 |
南瓜书 datawhalechina/pumpkin-book s |
【完整版-南京大学-机器学习】全66讲 OpenCV图像处理 58:28:56 |
南京大学周志华完整版100集【机器学习入门教程】人工智能-研究所 96:21:52 |
周志华《机器学习》西瓜书+李航《统计学习方法》 CV前沿与深度学习 54:56:53 |
南京大学人工智能学院院长周志华《机器学习西瓜书》白话解读，一起啃书！ AI技术星球 28:27:48 |
MLAPP
| Machine Learning A Probabilistic Perspective |
|---|
第四章 高斯模型 |
第五章 贝叶斯方法 |
第六章 频率统计方法 |
第七章 线性回归 |
第八章 逻辑回归 |
第九章 广义线性模型 |
第十章 有向图模型 |
第十一章 混合模型与EM算法 |
第十二章 隐线性模型 |
第十三章 稀疏线性模型 |
第十四章 Kernels |
第十五章 Gaussian Process |
第十六章 自适应基函数模型 |
第十七章 隐马尔可夫模型 |
第十八章 状态空间模型 |
第十九章 无向图模型 |
第二十章 图模型的确切推断 |
花书
MingchaoZhu/DeepLearning 数学推导、原理剖析与源码级别代码实现
百面深度学习
百面机器学习
统计学习方法
求推荐一部以李航的《统计学习方法》为教材的教学视频？知乎 |
深度之眼《统计学习方法》第二版啃书指导视频 深度之眼官方账号 08:55:48 |
《统计学习方法》第二版的代码实现 git |
《统计学习方法·第2版》手推公式+算法实例+Python实现 喜欢AI的程序猿 22h |
统计学习 Statistical Learning Stanford Online |
周志华《机器学习》西瓜书+李航《统计学习方法》 CV前沿与深度学习 54:56:53 |
PRML
PRML/PRMLT s Matlab code of machine learning algorithms in book PRML zh
ESL
其他
人工智能时代 李开复 Acsic People |
2020 Machine Learning Roadmap (still valid for 2021) Daniel Bourke |
Why AI is Harder Than We Think (Machine Learning Research Paper Explained) Yannic Kilcher |
Discovering ketosis: how to effectively lose weight git |
imhuay/studies 学习笔记 git |
25th-engineer/DaChuangFiles git |
MLEveryday/100-Days-Of-ML-Code 机器学习100天 en topic git git git |
How to Do Freelance AI Programming Siraj Raval |
Qinbf/deeplearning_paper |
Variational Autoencoders - EXPLAINED! CodeEmporium |
guillaume-chevalier/Awesome-Deep-Learning-Resources |
什么是 MLOps? Morgan Yong |
Productionize Your ML Workflows with MLOps Tools Weights & Biases |
ml-tooling/best-of-ml-python 项目包括：机器学习框架、数据可视化、图像、NLP和文本、图、金融领域、时间序列等等，内容非常全 |
7 FREE A.I. tools for YOU today! (plus 1 bonus!) Artificial Intelligence and Blockchain |
The Age of A.I. YouTube Originals |
The History of Artificial Intelligence [Documentary] Futurology — An Optimistic Future |
Artificial Intelligence: Exploring the Pros and Cons for a Smarter Future Things to Know |
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