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图宾根大学机器学习
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。
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．
Tensorflow Object Detection in 5 Hours with Python
[Tutorialsplanet.NET] Udemy - TensorFlow 2.0 Practical Advanced
PyTorch - Python Deep Learning Neural Network API
No Black Box Machine Learning Course – Learn Without Libraries
MIT公开课6.034 人工智能1 (带字幕) 唐逸豪
Stanford CS229: Machine Learning
[Tutorialsplanet.NET] Udemy -Artificial Intelligence with Python
[Tutorialsplanet.NET] Udemy - Machine Learning, Deep Learning and Bayesian Learning
54:47:36
2022 Fall 台大資訊 深度學習之應用 NTU CSIE ADL
MIT 6.S192：艺术、美学和创造力的深度学习
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
Greg Brockman OpenAI CEO CTO
动手学深度学习
数据科学
Python for Data Science - Course for Beginners (Learn Python, Pandas, NumPy, Matplotlib)
剪支 pruning
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)
数据科学读书会 Book 15 – 《Hands-on Time Series Analysis with Python》
Time Series Analysis：Data Scientist是如何做时间序列分析的？（第566期）
Time Series Forecasting Theory
Visualize Neural Networks
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。
LSTM
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
接受拒绝抽样 Acceptance Rejection Sampling
[Tutorialsplanet.NET] Udemy - Unsupervised Machine Learning Hidden Markov Models in Python
基于采样的马尔可夫链蒙特卡罗(Markov Chain Monte Carlo，简称MCMC)方法
Box-Muller算法？
接受拒绝抽样 Acceptance Rejection Sampling
metropolis Hastings
Gibbs Sampling
三个丑皮匠 顶个诸葛亮
[Tutorialsplanet.NET] Udemy - Ensemble Machine Learning in Python Random Forest, AdaBoost
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
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。
Momentum Contrast(MoCo)
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）
Ali Ghodsi, Lec : Deep Learning, Variational Autoencoder, Oct 12 2017 [Lect 6.2]
Variational Inference: Foundations and Modern Methods (NIPS 2016 tutorial)
label encoding, 把标签变成数字
过采样, oversampling smote
欠采样, undersampling EasyEnsemble
阈值移动, threshold moving
链接: https://pan.baidu.com/s/1CC6BFfiw0DcyVfYTofmH9A 提取码: r4z3
pyviz
Matplotlib
Plotly
[Tutorialsplanet.NET] Udemy - 2022 Python Data Analysis & Visualization Masterclass
[Tutorialsplanet.NET] Udemy - The Complete Data Visualization Course 2020
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。
a software version control visualization tool
电子教鞭
inux下netmeeting
红烛电子教鞭
deepin-draw
pointofix
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。
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
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Scc_hy
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人工智慧與數位教育中心 NCCU AIEC
解密遊俠
贪心学院 Greedy AI
Min Yuan
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深度之眼官方账号
Learning AI
財團法人人工智慧科技基金會
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Machine Learning with Phil
就是不吃草的羊
Artificial Intelligence and Blockchain
Colin Galen
Si磕AI论文的女算法 抖音号：
When Maths Meet Coding
Artificial Intelligence Society
Dr. Data Science
Pista Academy 波斯语
Parallel Computing and Scientific Machine Learning
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Machine Learning Street Talk
Dr Alan D. Thompson
Jeremy Howard
Artem Kirsanov
James Briggs
論文導讀
TeachMe AI
Priya Bhatia
大白话AI
Find Interesting AI
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Jeremy Howard: fast.ai Deep Learning Courses and Research | Podcast #35
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深度学习与TensorFlow 2入门实战
深度学习与TensorFlow 2
2022 Version of Applications of Deep Neural Networks for TensorFlow and Keras (Washington University in St. Louis)
I built the same model with TensorFlow and PyTorch | Which Framework is better?
AI框架基础
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人工智能 Tensorflow 视频教程全集| 5 小时从入门到精通
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TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial 6:52:07 Tech With Tim
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机器学习从零到一 TensorFlow
TensorFlow Lite 视频系列教程
深度学习应用开发-TensorFlow实践
TensorFlow 2.0
TensorFlow Lite 视频系列教程
TensorFlow 2 Beginner Course
Deep Learning for JavaScript Hackers | Use TensorFlow.js in the Browser
Made with TensorFlow.js
TensorFlow And Keras Tutorial | Deep Learning With TensorFlow & Keras | Deep Learning |
联想拯救者R9000P安装Ubuntu 21.04系统及运行TensorFlow1.X代码
Tensorflow
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Android Apps YOLOv4 TFLite Object Detection Android App Tutorial Using YOLOv4 Tiny, YOLOv4, and YOLOv4 Custom
深度学习框架Tensorflow2实战 唐宇迪
Learn TensorFlow and Deep Learning (beginner friendly code-first introduction)
Learn TensorFlow and Deep Learning fundamentals with Python (code-first introduction) Part 1/2 10:15:27
Learn TensorFlow and Deep Learning fundamentals with Python (code-first introduction) Part 2/2 3:57:54
Aladdin Persson
Deep Learning for Computer Vision with TensorFlow – Complete Course 1:13:16:40 colab
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Neural Network Programming - Deep Learning with PyTorch
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Deep Learning and Neural Networks with Python and Pytorch
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PyTorch Tutorials - Complete Beginner Course
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PyTorch - Deep Learning Course | Full Course | Session -1 | Python
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PyTorch on Apple Silicon | Machine Learning
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Learn PyTorch for deep learning in a day. Literally. 1:01:36:57
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Saving and Loading a PyTorch Neural Network (3.3)
I Built an A.I. Voice Assistant using PyTorch - part 1, Wake Word Detection
bentrevett/ PyTorch Seq2Seq
PyTorch 深度學習快速入門教程（絕對通俗易懂）| 土堆教程
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Data Augmentation, Regularization, and ResNets |
Image Generation using GANs |
PyTorch: Zero to GANs Dhanabhon
Deep Learning with PyTorch: Zero to GANs
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Keras - Python Deep Learning Neural Network API
Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial
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JAX
Intro to JAX: Accelerating Machine Learning research
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AppForAI-Windows 人工智慧開發工具
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人工智能：模型与算法 这个好
人工智能：模型与算法 - 浙江大学
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Clustering and Segmentation Algorithms explained
Machine Learning Tutorial Python | Machine Learning For Beginners
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【機器學習 2023】(生成式 AI) Autoregressive
【機器學習2022】
【機器學習2021】(中文版)
Next Step of Machine Learning (Hung-yi Lee, NTU, 2019)
Advanced Topics in Deep Learning (Hung-yi Lee, NTU) 2018
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Machine Learning From Scratch In Python - Full Course With 12 Algorithms (5 HOURS)
Machine Learning from Scratch - Python Tutorials Patrick Loeber
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End-To-End Data Science with Kaggle | Competition speed run?
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人工智能导论 浙江工业大学 共80课时 12小时15分33秒
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Introduction to Python in Google Colab and Introduction to Sci Kit Learn
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Machine learning in Python with scikit-learn
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Logistic Regression with Python | Titanic Data | Your First Kaggle Project |
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Community Talks on Day 2 | PyTorch Developer Day 2021
Stanford CS330: Deep Multi-Task and Meta Learning
Stanford CS330: Deep Multi-Task & Meta Learning I Autumn 2021I Professor Chelsea Finn
Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022
机器学习自动化调参
神经网络结构搜索 Neural Architecture Search
神经网络（十二） 自动神经网络（AutoML）与网络架构搜索（NAS）
Hyperparameter Tuning in Python with GridSearchCV
ROC Optimal Threshold ► Data Science Exercises #22
AutoML with Auto-Keras (14.1)
169 - Deep Learning made easy with AutoKeras
171 - AutoKeras for image classification using cifar10 data set
Automated Deep Learning with AutoKeras
I tried building a AUTO MACHINE LEARNING Web App 15 Minutes
Neural Architecture Search
Create Simple AutoML System from Scratch
CVPR'20 Interpretable Machine Learning Tutorial
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
SimCLR
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
MoCo 论文逐段精读【论文精读】 视觉 无监督表示学习 动量对比学习
对比学习论文综述【论文精读】
Meta Learning
Meta Learning
Meta-Learning and One-Shot Learning
Model Agnostic Meta Learning
Learning to learn: An Introduction to Meta Learning
Meta learning by Hugo
Ilya Sutskever: OpenAI Meta-Learning and Self-Play | MIT Artificial General Intelligence (AGI)
各種奇葩的元學習 (Meta Learning) 用法
【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
【機器學習2021】元學習 Meta Learning (二) - 萬物皆可 Meta
Few Shot Learning - EXPLAINED!
Few-shot learning in production
OpenAI's CLIP for Zero Shot Image Classification
Fast Zero Shot Object Detection with OpenAI CLIP
Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
神经网络（四） 注意力机制
RNN模型与NLP应用
Transformer模型
【機器學習 2022】各式各樣神奇的自注意力機制 (Self-attention) 變型
Attention in Neural Networks
机器学习常用损失函数小结
机器学习如何选择回归损失函数的？
神经网络的损失函数为什么是非凸的?
Chaoyang He no瞎哔哔
联邦学习：技术角度的讲解（中文）Introduction to Federated Learning
杨强 | 用户隐私，数据孤岛和联邦学习
刘洋丨联邦学习的技术挑战和应用展望
分布式机器学习
FedML联邦机器学习开源框架视频教程全集
[Tutorial] FedML: a research library for federated machine learning
90秒入门联邦学习 Federated learning
什么是联邦学习（Federated Learning）？【知多少】
详解联邦学习Federated Learning - 知乎
联邦学习与个性化联邦学习
5 concepts of A/B testing you should know as a Data Scientist
How to run A/B Tests as a Data Scientist!
AB Testing概览
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
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）
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）
AB test calculator (pet project) | Gleb Builds #2
Phoneme Detection with CNN-RNN-CTC Loss Function - Machine Learning
CTC for Offline Handwriting Recognition
F18 Recitation 8: Connectionist Temporal Classification (CTC)
S18 Lecture 14: Connectionist Temporal Classification (CTC)
数据科学读书会 Book 17 – 因果推断 因果效应(Causal Effect)
数据科学读书会 Book 17 - 因果推断-因果推断的公式和模型
探索因果规律之因果推断基础（ft. The Book of Why by Judea Pearl)
因果效应学习基础
《为什么》关于因果关系的新科学
【数之道 04】解决最优路径问题的妙招-蚁群ACO算法
What is an Autoencoder? | Two Minute Papers #86
Simple Explanation of AutoEncoders
Autoencoders - EXPLAINED
What are Autoencoders?
Autoencoders - Ep. 10 (Deep Learning SIMPLIFIED)
85a - What are Autoencoders and what are they used for?
Understanding and Applying Autoencoders in Python!
85b - An introduction to autoencoders - in Python
Autoencoder Dimensionality Reduction Python TensorFlow / Keras #CodeItQuick
Autoencoders Explained Easily
Autoencoders Made Simple!
Variational Autoencoders
Variational Autoencoders - EXPLAINED!
Autoencoder Explained
178 - An introduction to variational autoencoders (VAE)
179 - Variational autoencoders using keras on MNIST data
VAE-GAN Explained!
What are Generative Models? | VAE & GAN | Intro to AI
Build a Stable Diffusion VAE From Scratch using Pytorch
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。
如何简单易懂地理解变分推断(variational inference)？
变分推断与变分自编码器
The EM Algorithm
人工智慧在臺灣：產業轉型的契機與挑戰｜陳昇瑋研究員
BUILD and SELL your own A.I Model! $500 - $10,000/month (super simple!)
Machine Learning Projects You NEVER Knew Existed
Data Collection Project Ideas & Demos
ROC and AUC, Clearly Explained!
145 - Confusion matrix, ROC and AUC in machine learning
实操揭秘数据标注项目的套路，有点得罪人了，阅后删
Image Annotation for Machine Learning
RubanSeven/
149 - Working with imbalanced data for ML - Demonstrated using liver disease data
类别不平衡 南京大学周志华
Data Visualization with D3 – Full Course for Beginners [2022]
Data Visualization with D3.js - Full Tutorial Course() 老版本
Other Level’s
DataV Vue
CNN Explainer
dair-ai/
Visualization and Interactive Dashboard in Python: My favorite Python Viz tools — HoloViz
Viz
ContextLab/ 用于获得对高维数据的几何洞察力的 Python 工具箱
Vispy
How to Create a Beautiful Python Visualization Dashboard With Panel/Hvplot
szagoruyko/ colab
Data Visualization Using Python BOKEH | Python Bokeh Dashboard | Full Course
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）
Automatically Visualize Datasets with AutoViz in Python
EdrawMax
Python Data Analysis Projects for 2022 | Data Analysis With Python | Python Training |
Build a Media Analysis Dashboard with Python & Cloudinary
Longer lessons
Data Visualisation
Interactive Web Visualizations with Bokeh in Python
Visualizing Binary Data with 7-Segment Displays
🔴 Visualizing Data Structures and Algorithms with VS Code
Data Visualization Tutorial using Qliksense
Data Visualisation
D3 JS - Build Data Driven Visualizations with Javascript [svg animation, data engineering]
Plotnine: A Different Approach To Data Visualization in Python
7 Python Data Visualization Libraries in 15 minutes
Machine Learning Course - Lesson 2: Visualizing Data with JavaScript
Create Interactive Maps & Geospatial Data Visualizations With Python | Real Python Podcast #143
Build a Chart using JavaScript (No Libraries)
Machine Learning Model Evaluation in JavaScript Radu
Machine Learning Course
Tableau in Two Minutes - Tableau Basics for Beginners
How to create Radial Chart in Tableau| Step-by-step
Tableau数据可视化，学完就掌握商业分析必备技能了！（第613期）
Tableau零基础教程
Gourcer
How to Deploy Machine Learning Apps?
Kevin Goetsch | Deploying Machine Learning using sklearn pipelines
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
Deploy ML Models from Colab with FastAPI & ColabCode - Free ML as a Service
Run Your Flask App In Google Colab | [ Updated Way ]
How to run Google Colab or Kaggle notebooks on VSCODE (My experience running example code on GPU)
Deploying production ML models with TensorFlow Serving overview
Deployment of ML Models
Aladdin Persson
Build & Deploy AI SaaS with Reoccurring Revenue (Next.js, OpenAI, Stripe, Tailwind, Vercel)
How to Deploy a Web App Using Multiple Methods (Azure, Render, MongoDB Atlas, Koyeb, and more )
👍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
Deploy Python Applications - Google Cloud Run with Docker
Deploy Python Applications From Source - Google Cloud Run
Edge AI 開發板挑選完整指南
NVIDIA TensorRT: High Performance Deep Learning Inference
【AIGC】七千字通俗讲解Stable Diffusion | 稳定扩散模型 | CLIP | UNET | VAE | Dreambooth | LoRA
Talk | 北京大学杨灵：扩散生成模型的方法、关联与应用
Diffusion models explained in 4-difficulty levels
DDPM - Diffusion Models Beat GANs on Image Synthesis (Machine Learning Research Paper Explained)
Ultimate Guide to Diffusion Models | ML Coding Series | Denoising Diffusion Probabilistic Models
Diffusion models
Exploring the NEW Hugging Face Diffusers Package | Diffusion Models w/ Python
Stable Diffusion - What, Why, How? 54:07 colab
由浅入深了解Diffusion Model
Creating Stable Diffusion Interpolation Videos
midjourney
[ML News] Stable Diffusion Takes Over! (Open Source AI Art)
Stable Diffusion AI画图
ERNIE-ViLG s
CompVis/
Harmonai, Dance Diffusion and The Audio Generation Revolution
AI艺术 抖音号： askNK
Google's AI: Stable Diffusion On Steroids! 💪
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
Diffusion Models | Paper Explanation | Math Explained
Diffusion models from scratch in PyTorch
JEPA - A Path Towards Autonomous Machine Intelligence (Paper Explained)
Google's DreamFusion AI: Text to 3D sentdexGoogle's DreamFusion AI: Text to 3D
I tried to build a REACT STABLE DIFFUSION App in 15 minutes
Stable Diffusion Is Getting Outrageously Good! 🤯
Stable Diffusion Version 2: Power To The People… For Free!
[ML News] Multiplayer Stable Diffusion | OpenAI needs more funding | Text-to-Video models incoming
Google's Prompt-to-Prompt: Diffusion Image Editing
Diffusion Model 수학이 포함된 tutorial
Stable Diffusion in Code (AI Image Generation) -
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
Diffusion and Score-Based Generative Models
Generative Adversarial Networks (GANs) and Stable Diffusion
Diffusion Models - Live Coding Tutorial
Diffusion Models - Live Coding Tutorial 2.0
Kas Kuo Lab
MIT 6.S192 - Lecture 22: Diffusion Probabilistic Models, Jascha Sohl-Dickstein
Diffusion Models for Inverse Problems
Planning with Diffusion for Flexible Behavior Synthesis
Hierarchically branched diffusion models
Diffusion models as plug-and-play priors
Tutorial on Denoising Diffusion-based Generative Modeling: Foundations and Applications
【stable diffusion】由淺入深了解Diffusion擴散模型 唐宇迪
AI Art Taking World By Storm - Diffusion Models Overview
AI Art for Beginners - Stable Diffusion Crash Course
CS 198-126: Lecture 12 - Diffusion Models
What are Diffusion Models?
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
[專題解說] Introduction to Diffusion Model 擴散模型入門 [附程式碼] 教學
號稱打敗 GAN 的生成模型: Diffusion Models
Stable Diffusion Online
CompVis/
AI Art with Stable Diffusion (Women of the World)
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！ |
Generating Realistic AI Images with Stable Diffusion
为什么AI画画能既离谱又烧钱啊？？
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
Lesson 9: Deep Learning Foundations to Stable Diffusion, 2022
由Stabiliti AI在2022年发布的工具 抓取了50亿公开图片, 可以用文字和图片生成图片 colab Chillout_mix
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
AI For You
Easiest Way To Install Stable Diffusion & Generate AI Images
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）
Stable Diffusion XL
JW608 Plays With Stable Diffusion!
[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!
Stable Diffusion教學 使用Lora製作AI網紅
Stable Diffusion 教學
AI绘画】给美女们更换衣服
Stable Diffusion Tutorials, Automatic1111 Web UI & Google Colab Guides, DreamBooth, Textual Inversion / Embedding, LoRA, AI Upscaling, Video to Anime
Stable Diffusion Got Supercharged - For Free!
生成扩散模型漫谈：条件控制生成结果 有参考文献
生成扩散模型漫谈（九）：条件控制生成结果
生成扩散模型漫谈（十七）：构建ODE的一般步骤（下）
工gin師
DiffusionModel
Stable Diffusion 系列 工
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.
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
Stable Diffusion教程 从入门到精通
Stable Diffusion 电商系列课程
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
| 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: Fusing Diffusion Paths for Controlled Image Generation
pkuliyi2015/
高清放大插件MultiDiffusion 小显存也能跑出4k图 低配福音
火遍全网的AI大模型，华为能搞出什么新花样？
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
#84 LAURA RUIS - Large language models are not zero-shot communicators [NEURIPS UNPLUGGED]
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
Real World Applications of Large Models
Foundation models and the next era of AI
Emily M. Bender — Language Models and Linguistics
多模态论文串讲·上【论文精读】
多模态论文串讲·下【论文精读】
CLIP 论文逐段精读【论文精读】
CLIP 改进工作串讲（上）【论文精读】
CLIP 改进工作串讲（下）【论文精读】
ViLT 论文精读【论文精读】
ViT论文逐段精读【论文精读】 mli/
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Paper Explained)
AI Hairball - ChatGPT + Stable Diffusion
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
OpenAI CLIP Explained | Multi-modal ML
Fast Zero Shot Object Detection with OpenAI CLIP
OpenAI's CLIP for Zero Shot Image Classification
Fast intro to multi-modal ML with OpenAI's CLIP
OpenAI CLIP: ConnectingText and Images (Paper Explained)
Domain-Specific Multi-Modal Machine Learning with CLIP
CLIP: Connecting Text and Images
OpenAI CLIP - Connecting Text and Images | Paper Explained
OpenAI’s CLIP explained! | Examples, links to code and pretrained model
Talk | 微软高级研究员杨征元：统一的视觉语言模型
Vision Transformer (ViT) 用于图片分类
Vision Transformers (ViT) Explained + Fine-tuning in Python
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。
【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
facebookresearch/
ImageBind: a new way to ‘link’ AI across the senses
AI Safety
【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)
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
Talk | 几何的魅力: 黑盒攻击新策略
Steven Van Vaerenbergh
CompSci 188
神经网络与深度学习(, , )
复旦大学邱锡鹏教授的《神经网络与深度学习》 19:05:43
HyperDL-Tutorial(, , )
机器学习实战（Machine Learning in Action） (, )
Interpretable Machine Learning (, )
ML Kit 中文文档 (, )
spark机器学习算法研究和源码分析 (, )
ml5.js - Machine Learning for Web (, )
机器学习训练秘籍（Machine Learning Yearning 中文版） (, )
Pipcook v1.0 机器学习工具使用教程 (, )
(, , )
Deeplearning Algorithms Tutorial（深度学习算法教程） (, , )
花书 deeplearningbook(, )
（知乎）
awesome-material
foochane/books
lovingers/ML_Books 差评
深度学习入门-基于Python的理论与实现 deep-learning-from-scratch
【一起啃书】机器学习西瓜书白话解读 13:10:47
周志华《机器学习》学习笔记
南瓜书 datawhalechina/
南京大学周志华教授亲讲 6:20:50
机器学习初步- 南京大学-
机器学习-周志华-学习记录-第一章绪论 csdn
【完整版-南京大学-机器学习】全66讲 58:28:56
南京大学周志华完整版100集【机器学习入门教程】 96:21:52
周志华《机器学习》西瓜书+李航《统计学习方法》 54:56:53
第一章 介绍
第二章 概率
第三章 基于离散数据的生成模型
第四章
第五章
第六章
第七章
第八章
第九章
第十章
第十一章
第十二章
第十三章
第十四章
第十五章
第十六章
第十七章
第十八章
第十九章
第二十章
MingchaoZhu/ 数学推导、原理剖析与源码级别代码实现
求推荐一部以李航的《统计学习方法》为教材的教学视频？
深度之眼《统计学习方法》第二版啃书指导视频 08:55:48
大数据机器学习（袁春） 共113课时 15小时39分33秒
《统计学习方法》第二版的代码实现
《统计学习方法·第2版》手推公式+算法实例+Python实现 22h
统计学习 Statistical Learning
周志华《机器学习》西瓜书+李航《统计学习方法》 54:56:53
PRML/P Matlab code of machine learning algorithms in book PRML
人工智能时代 李开复
2020 Machine Learning Roadmap (still valid for 2021)
Why AI is Harder Than We Think (Machine Learning Research Paper Explained)
Discovering ketosis: how to effectively lose weight
imhuay/studies 学习笔记
25th-engineer/DaChuangFiles
MLEveryday/ 机器学习100天 git
How to Do Freelance AI Programming
Qinbf/
Variational Autoencoders - EXPLAINED!
guillaume-chevalier/
什么是 MLOps?
MLOps
Productionize Your ML Workflows with MLOps Tools
ml-tooling/ 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!)
The Age of A.I.
The History of Artificial Intelligence [Documentary] Futurology —
Artificial Intelligence: Exploring the Pros and Cons for a Smarter Future
From Deep Learning of Disentangled Representations to Higher-level Cognition
A Fireside Chat with Turing Award Winner Geoffrey Hinton, Pioneer of Deep Learning (Google I/O'19)
Geoff Hinton explains the Forward-Forward Algorithm
Geoff Hinton on Forward-Forward
This Algorithm Could Make a GPT-4 Toaster Possible
Full interview: "Godfather of artificial intelligence" talks impact and potential of AI
Geoffrey Hinton: The Foundations of Deep Learning
深入学习英雄: 吴恩达采访 Geoffrey Hinton
This Canadian Genius Created Modern AI
Andrew Ng: Deep Learning, Education, and Real-World AI | Podcast #73
Andrew Ng: Advice on Getting Started in Deep Learning | AI Podcast Clips
Michael I. Jordan: Machine Learning, Recommender Systems, and Future of AI | Podcast #74
: "A Path Towards Autonomous AI", Baidu 2022-02-22
Yann LeCun: Deep Learning, ConvNets, and Self-Supervised Learning | Podcast #36
Yann LeCun: Dark Matter of Intelligence and Self-Supervised Learning | Podcast #258
Yann LeCun Lecture 8/8 Unsupervised Learning
John Carmack will Develop True Artificial Intelligence. Here is Why
Is ChatGPT A Step Toward Human-Level AI? — With Yann LeCun, Meta Chief AI Scientist
北风网Python人工智能-
北风网Python人工智能-
北风网Python人工智能-
北风网Python人工智能-
北风网Python人工智能-
北风网Python人工智能-
北风网Python人工智能-
北风网Python人工智能-
北风网的大数据时代的
麦子人工智能视频教程(：Python数据分析与建模库)
麦子人工智能视频教程(：机器学习经典算法)
麦子人工智能视频教程(：机器学习案例实战)