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斯坦福大学《统计学习导论2023Python版|An Introduction to Statistical Learning with Python》中英字幕

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2024-02-01 17:57:09

https://www.youtube.com/watch?v=LvySJGj-88U&list=PLoROMvodv4rPP6braWoRt5UCXYZ71GZIQ&pp=iAQB 随着数据收集的规模和范围在几乎所有领域持续增加，统计学习已成为任何希望了解数据的人的关键工具包。《统计学习导论》对统计学习的关键主题进行了广泛而不太技术性的介绍。该书适合任何希望使用当代数据分析工具的人。该书的第一版，以R语言应用为例（ISLR），于2013年出版。第二版《ISLR》于2021年出版。已经有中文、意大利文、日文、韩文、蒙古文、俄文和越南文的翻译版本。Python版本（ISLP）于2023年出版。每个版本的书末都有一个实验室，展示了该章节中的概念在R或Python中的应用。各章节涵盖以下主题：什么是统计学习？回归分类重采样方法线性模型选择和正则化超越线性基于树的方法支持向量机深度学习生存分析无监督学习多重检验

斯坦福大学

Robert Tibshirani

machine learning

statistical learning

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[10]A Demo of Athena for CSCE 585 (Course Project Overview).zh_en

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[03] Winter 2021 ｜ Lecture 3 - Backprop and Neural Networks.zh_en

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[06] Winter 2021 ｜ Lecture 6 - Simple and LSTM RNNs.zh_en

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[08] 2023 ｜ Lecture 8 - Self-Attention and Transformers.zh_en

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[09] 2023 ｜ Lecture 9 - Pretraining.zh_en

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[11] 2023 ｜ Lecture 11 - Natural Language Generation.zh_en

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[12] Winter 2021 ｜ Lecture 12 - Question Answering.zh_en

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[13] Winter 2021 ｜ Lecture 13 - Coreference Resolution.zh_en

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[001]Statistical Learning_ 1.1 Opening Remarks.zh_en

18:19

[002]Statistical Learning_ 8 Years Later (Second Edition of the Course).zh_en

02:19

[003]Statistical Learning I Introducing Jonathan - Third Edition of the Course I

01:48

[004]Statistical Learning_ 1.2 Examples and Framework.zh_en

12:13

[005]Statistical Learning_ 2.1 Introduction to Regression Models.zh_en

11:42

[006]Statistical Learning_ 2.2 Dimensionality and Structured Models.zh_en

11:41

[007]Statistical Learning_ 2.3 Model Selection and Bias Variance Tradeoff.zh_en

10:05

[008]Statistical Learning_ 2.4 Classification.zh_en

15:38

[009]Statistical Learning_ 2.Py Setting Up Python I 2023.zh_en

05:12

[010]Statistical Learning_ 2.Py Data Types, Arrays, and Basics I 2023.zh_en

16:42

[011]Statistical Learning_ 2.Py.3 Graphics I 2023.zh_en

06:07

[012]Statistical Learning_ 2.Py Indexing and Dataframes I 2023.zh_en

10:21

[013]Statistical Learning_ 3.1 Simple linear regression.zh_en

13:02

[014]Statistical Learning_ 3.2 Hypothesis Testing and Confidence Intervals.zh_en

08:25

[015]Statistical Learning_ 3.3 Multiple Linear Regression.zh_en

15:38

[016]Statistical Learning_ 3.4 Some important questions.zh_en

14:52

[017]Statistical Learning_ 3.5 Extensions of the Linear Model.zh_en

14:17

[018]Statistical Learning_ 3.Py Linear Regression and statsmodels Package I 2023

09:10

[019]Statistical Learning_ 3.Py Multiple Linear Regression Package I 2023.zh_en

02:26

[020]Statistical Learning_ 3.Py Interactions, Qualitative Predictors and Other D

06:33

[021]Statistical Learning_ 4.1 Introduction to Classification Problems.zh_en

10:26

[022]Statistical Learning_ 4.2 Logistic Regression.zh_en

09:08

[023]Statistical Learning_ 4.3 Multivariate Logistic Regression.zh_en

09:54

[024]Statistical Learning_ 4.4 Logistic Regression Case Control Sampling and Mul

07:29

[025]Statistical Learning_ 4.5 Discriminant Analysis.zh_en

07:13

[026]Statistical Learning_ 4.6 Gaussian Discriminant Analysis (One Variable).zh_

07:38

[027]Statistical Learning_ 4.7 Gaussian Discriminant Analysis (Many Variables).z

17:43

[028]Statistical Learning_ 4.8 Generalized Linear Models.zh_en

09:35

[029]Statistical Learning_ 4.9 Quadratic Discriminant Analysis and Naive Bayes.z

10:08

[030]Statistical Learning_ 4.Py Logistic Regression I 2023.zh_en

11:44

[031]Statistical Learning_ 4.Py Linear Discriminant Analysis (LDA) I 2023.zh_en

09:58

[032]Statistical Learning_ 4.Py K-Nearest Neighbors (KNN) I 2023.zh_en

07:06

[033]Statistical Learning_ 5.1 Cross Validation.zh_en

14:02

[034]Statistical Learning_ 5.2 K-fold Cross Validation.zh_en

13:34

[035]Statistical Learning_ 5.3 Cross Validation the wrong and right way.zh_en

10:08

[036]Statistical Learning_ 5.4 The Bootstrap.zh_en

11:30

[037]Statistical Learning_ 5.5 More on the Bootstrap.zh_en

14:36

[038]Statistical Learning_ 5.Py Cross-Validation I 2023.zh_en

10:00

[039]Statistical Learning_ 5.Py Bootstrap I 2023.zh_en

05:34

[040]Statistical Learning_ 6.1 Introduction and Best Subset Selection.zh_en

13:45

[041]Statistical Learning_ 6.2 Stepwise Selection.zh_en

12:27

[042]Statistical Learning_ 6.3 Backward stepwise selection.zh_en

05:27

[043]Statistical Learning_ 6.4 Estimating test error.zh_en

14:07

[044]Statistical Learning_ 6.5 Validation and cross validation.zh_en

08:44

[045]Statistical Learning_ 6.6 Shrinkage methods and ridge regression.zh_en

12:38

[046]Statistical Learning_ 6.7 The Lasso.zh_en

15:22

[047]Statistical Learning_ 6.8 Tuning parameter selection.zh_en

05:28

[048]Statistical Learning_ 6.9 Dimension Reduction Methods.zh_en

04:46

[049]Statistical Learning_ 6.10 Principal Components Regression and Partial Leas

15:49

[050]Statistical Learning_ 6.Py Stepwise Regression I 2023.zh_en

14:06

[051]Statistical Learning_ 6.Py Ridge Regression and the Lasso I 2023.zh_en

19:09

[052]Statistical Learning_ 7.1 Polynomials and Step Functions.zh_en

15:00

[053]Statistical Learning_ 7.2 Piecewise Polynomials and Splines.zh_en

13:14

[054]Statistical Learning_ 7.3 Smoothing Splines.zh_en

10:11

[055]Statistical Learning_ 7.4 Generalized Additive Models and Local Regression.

10:46

[056]Statistical Learning_ 7.Py Polynomial Regressions and Step Functions I 2023

08:19

[057]Statistical Learning_ 7.Py Splines I 2023.zh_en

03:12

[058]Statistical Learning_ 7.Py Generalized Additive Models (GAMs) I 2023.zh_en

08:15

[059]Statistical Learning_ 8.1 Tree based methods.zh_en

14:38

[060]Statistical Learning_ 8.2 More details on Trees.zh_en

11:46

[061]Statistical Learning_ 8.3 Classification Trees.zh_en

11:01

[062]Statistical Learning_ 8.4 Bagging.zh_en

13:46

[063]Statistical Learning_ 8.5 Boosting.zh_en

12:03

[064]Statistical Learning_ 8.6 Bayesian Additive Regression Trees.zh_en

11:34

[065]Statistical Learning_ 8.Py Tree-Based Methods I 2023.zh_en

15:31

[066]Statistical Learning_ 9.1 Optimal Separating Hyperplane.zh_en

11:36

[067]Statistical Learning_ 9.2.Support Vector Classifier.zh_en

08:05

[068]Statistical Learning_ 9.3 Feature Expansion and the SVM.zh_en

15:05

[069]Statistical Learning_ 9.4 Example and Comparison with Logistic Regression.z

14:48

[070]Statistical Learning_ 9.Py Support Vector Machines I 2023.zh_en

18:58

[071]Statistical Learning_ 9.Py ROC Curves I 2023.zh_en

04:45

[072]Statistical Learning_ 10.1 Introduction to Neural Networks.zh_en

15:31

[073]Statistical Learning_ 10.2 Convolutional Neural Networks.zh_en

17:09

[074]Statistical Learning_ 10.3 Document Classification.zh_en

07:47

[075]Statistical Learning_ 10.4 Recurrent Neural Networks.zh_en

14:45

[076]Statistical Learning_ 10.5 Time Series Forecasting.zh_en

16:51

[077]Statistical Learning_ 10.6 Fitting Neural Networks.zh_en

17:04

[078]Statistical Learning_ 10.7 Interpolation and Double Descent.zh_en

11:12

[079]Statistical Learning_ 10.Py Single Layer Model_ Hitters Data I 2023.zh_en

17:12

[080]Statistical Learning_ 10.Py Multilayer Model_ MNIST Digit Data I 2023.zh_en

07:49

[081]Statistical Learning_ 10.Py Convolutional Neural Network_ CIFAR Image Data

10:13

[082]Statistical Learning_ 10.Py Document Classification and Recurrent Neural Ne

09:32

[083]Statistical Learning_ 11.1 Introduction to Survival Data and Censoring.zh_e

14:11

[084]Statistical Learning_ 11.2 Proportional Hazards Model.zh_en

14:32

[085]Statistical Learning_ 11.3 Estimation of Cox Model with Examples.zh_en

13:43

[086]Statistical Learning_ 11.4 Model Evaluation and Further Topics.zh_en

06:13

[087]Statistical Learning_ 11.Py Cox Model_ Brain Cancer Data I 2023.zh_en

16:52

[088]Statistical Learning_ 11.Py Cox Model_ Publication Data I 2023.zh_en

04:59

[089]Statistical Learning_ 12.1 Principal Components.zh_en

12:37

[090]Statistical Learning_ 12.2 Higher order principal components.zh_en

17:40

[091]Statistical Learning_ 12.3 k means Clustering.zh_en

17:18

[092]Statistical Learning_ 12.4 Hierarchical Clustering.zh_en

14:46

[093]Statistical Learning_ 12.5 Matrix Completion.zh_en

15:53

[094]Statistical Learning_ 12.6 Breast Cancer Example.zh_en

09:25

[095]Statistical Learning_ 12.Py Principal Components I 2023.zh_en

11:27

[096]Statistical Learning_ 12.Py Clustering I 2023.zh_en

11:22

[097]Statistical Learning_ 12.Py Application_ NCI60 Data I 2023.zh_en

12:26

[098]Statistical Learning_ 13.1 Introduction to Hypothesis Testing.zh_en

14:32

[099]Statistical Learning_ 13.1 Introduction to Hypothesis Testing II.zh_en

10:18

[100]Statistical Learning_ 13.2 Introduction to Multiple Testing and Family Wise

12:31

[101]Statistical Learning_ 13.3 Bonferroni Method for Controlling FWER.zh_en

06:32

[102]Statistical Learning_ 13.4 Holms Method for Controlling FWER.zh_en

05:57

[103]Statistical Learning_ 13.5 False Discovery Rate and Benjamini Hochberg Meth

11:14

[104]Statistical Learning_ 13.6 Resampling Approaches.zh_en

03:21

[105]Statistical Learning_ 13.6 Resampling Approaches II.zh_en

07:44

[106]Statistical Learning_ 13.Py Multiple Testing I 2023.zh_en

17:01

[107]Statistical Learning_ 13.Py False Discovery Rate I 2023.zh_en

06:09

[108]Statistical Learning_ 13.Py Multiple Testing and Resampling I 2023.zh_en

06:12

吴恩达《机器学习工程师的生产实践（MLOps）Machine Learning Engineering for Production》中英字幕

TrevorHastie/RobertTibshirani《统计学习导论|An Introduction to Statistical Learning》中英字

吴恩达《机器学习》|machine-learning（中英字幕-deepseek翻译）

吴恩达《自然语言处理|natural language processing》中英字幕

UCB《人工智能导论|UCB CS 188 Introduction to Artificial Intelligence 2025》中英字幕

UCB《机器学习导论|UCB CS 189 Introduction to Machine Learning Fall 2024》中英字幕

斯坦福大学《CS25 V5 Transformer United》Claude-3.7翻译

纽约大学《深度学习|NYU Deep learning DS-GA 1008 2021》中英字幕（Claude-3.7-s

[01]01 – History and resources.zh_en

50:18

[02]01L – Gradient descent and the backpropagation algorithm.zh_en

01:51:04

[03]02 – Neural nets_ rotation and squashing.zh_en

01:01:54

[04]02L – Modules and architectures.zh_en

01:42:27

[05]03 – Tools, classification with neural nets, PyTorch implementation.zh_en

01:05:48

[06]03L – Parameter sharing_ recurrent and convolutional nets.zh_en

01:59:48

[07]04L – ConvNet in practice.zh_en

51:41

[08]04.1 – Natural signals properties and the convolution.zh_en

01:09:13

[09]04.2 – Recurrent neural networks, vanilla and gated (LSTM).zh_en

01:05:37

[10]05L – Joint embedding method and latent variable energy based models (LV-EBM

01:51:32

[11]05.1 – Latent Variable Energy Based Models (LV-EBMs), inference.zh_en

01:01:05

[12]05.2 – But what are these EBMs used for_.zh_en

10:43

[13]06L – Latent variable EBMs for structured prediction.zh_en

01:48:54

[14]06 – Latent Variable Energy Based Models (LV-EBMs), training.zh_en

01:04:49

[15]07L – PCA, AE, K-means, Gaussian mixture model, sparse coding, and intuitive

01:54:23

[16]07 – Unsupervised learning_ autoencoding the targets.zh_en

56:42

[17]08L – Self-supervised learning and variational inference.zh_en

01:54:44

[18]08 – From LV-EBM to target prop to (vanilla, denoising, contractive, variati

01:00:35

[19]09L – Differentiable associative memories, attention, and transformers.zh_en

02:00:29

[20]09 – AE, DAE, and VAE with PyTorch; generative adversarial networks (GAN) an

01:07:51

[21]10L – Self-supervised learning in computer vision.zh_en

01:36:14

[22]10 – Self _ cross, hard _ soft attention and the Transformer.zh_en

01:12:01

[23]11L – Speech recognition and Graph Transformer Networks.zh_en

01:55:04

[24]11 – Graph Convolutional Networks (GCNs).zh_en

57:34

[26]12 – Planning and control.zh_en

01:10:23

[27]13L – Optimisation for Deep Learning.zh_en

01:51:33

[28]13 – The Truck Backer-Upper.zh_en

01:01:22

[29]14L – Lagrangian backpropagation, final project winners, and Q&A session.zh_

02:12:36

[30]14 – Prediction and Planning Under Uncertainty.zh_en

01:14:45

[32]09P – Contrastive joint embedding methods (JEMs) for self-supervised learnin

56:52

[33]10P – Non-contrastive joint embedding methods (JEMs) for self-supervised lea

01:05:28

罗格斯大学《AI概念和策略|AI Concepts and Strategy》中英字幕

罗格斯大学《商业中的人工智能伦理|AI Ethics in Business》中英字幕

约翰霍普金斯大学《神经网络基础|Foundations of Neural Networks》中英字幕

约翰霍普金斯大学《应用机器学习|Applied Machine Learning》中英字幕

UCB《计算机视觉与计算摄影导论|CS180 Intro to Computer Vision and Computational Photography》

01:24:00

斯坦福大学《计算机视觉的深度学习|CS231n: Deep Learning for Computer Vision 2025》中英字幕

吴恩达《深度学习（2-5)|deep-learning（中英字幕）》Claude-3.7-s

吴恩达《深度学习|deep learning（coursera）》中英字幕（deepseek

《深度学习|6.S191 Introduction to Deep Learning 2025》中英字幕

《统计学习理论和应用|9.520 6.860 Statistical Learning Theory and Applications 2019》中英字幕

斯坦福大学《深度学习|CS230 Deep Learning 2025》中英字幕

UCB《人工智能导论|CS 188. Introduction to Artificial Intelligence 2025 Fall》中英字幕

牛津大学《深度学习|Deep learning 2015》中英字幕（Claude-4-s）

CMU《自然语言处理|11-411 11-611 Natural Language Processing 2020 Fall》中英字幕（Claude-4-S）

吴恩达《Pytorch深度学习（4模块全）|PyTorch for Deep Learning》中英字幕

UBC《机器学习（研究生）|CPSC 540 Machine learning 2013》中英字幕

UBC《机器学习和数据挖掘|CPSC 340 Machine Learning and Data Mining 2012》中英字幕

[01]undergraduate machine learning 1_ Introduction to machine learning.zh_en

46:17

[02]undergraduate machine learning 2_ Introduction to machine learning 2.zh_en

53:54

[03]undergraduate machine learning 3_ Basic probability.zh_en

53:54

[04]undergraduate machine learning 4_ Introduction to probability, linear algebr

53:55

[05]undergraduate machine learning 5_ Introduction to Bayes.zh_en

09:30

[06]undergraduate machine learning 6_ Bayes rule and Bayesian networks.zh_en

51:44

[07]undergraduate machine learning 8_ Inference in Bayesian networks and dynamic

49:50

[08]undergraduate machine learning 9_ Hidden Markov models - HMM.zh_en

52:23

[09]undergraduate machine learning 10_ Expectation, probability and Bernoulli mo

42:21

[10]undergraduate machine learning 11_ Maximum likelihood.zh_en

32:55

[11]undergraduate machine learning 12_ Bayesian learning.zh_en

53:10

[12]undergraduate machine learning 13_ Learning Bayesian networks.zh_en

53:28

[13]undergraduate machine learning 14_ Linear algebra revision for machine learn

52:38

[14]undergraduate machine learning 15_ Singular Value Decomposition - SVD.zh_en

51:56

[15]undergraduate machine learning 16_ Principal Component Analysis - PCA.zh_en

51:32

[16]undergraduate machine learning 18_ Least squares and the multivariate Gaussi

42:59

[17]undergraduate machine learning 7_ Bayesian networks, aka probabilistic graph

45:00

[18]undergraduate machine learning 17_ Linear prediction.zh_en

51:16

[19]undergraduate machine learning 20_ Cross-validation, big data and regulariza

01:30:53

[20]undergraduate machine learning 21_ L1 regularization and the lasso.zh_en

20:20

[21]undergraduate machine learning 22_ Sparse models and variable selection.zh_

01:11:40

[22]undergraduate machine learning 22_ Sparse models and variable selection.zh_

01:11:40

[23]undergraduate machine learning 23_ Dirichlet and categorical distributions.z

46:40

[24]undergraduate machine learning 24_ Text classification with Naive Bayes.zh_e

43:49

[25]undergraduate machine learning 25_ Twitter sentiment prediction with Naive B

48:39

[26]undergraduate machine learning 26_ Optimization.zh_en

49:02

[27]undergraduate machine learning 27_ Logistic regression.zh_en

51:13

[28]undergraduate machine learning 28_ Neural networks.zh_en

26:20

[29]undergraduate machine learning 29_ Neural nets and backpropagation.zh_en

40:14

[30]undergraduate machine learning 30_ Deep learning.zh_en

50:05

[31]undergraduate machine learning 31_ Decision trees.zh_en

39:42

[32]undergraduate machine learning 32_ Random forests.zh_en

33:11

[33]undergraduate machine learning 33_ Random forests, face detection and Kinect

38:29

吴恩达《Jupyter AI：在笔记本中进行人工智能编程|Jupyter AI AI Coding in Notebooks》

Hinton@多伦多大学《机器学习的神经网络|Neural Networks for Machine Learning 2016》claude-4s

[01]Lecture 1.1 — Why do we need machine learning [Neural Networks for Machine

13:15

[02]Lecture 1.2 — What are neural networks [Neural Networks for Machine Learnin

08:31

[03]Lecture 1.3 — Some simple models of neurons [Neural Networks for Machine Le

08:24

[04]Lecture 1.4 — A simple example of learning [Neural Networks for Machine Lea

05:39

[05]Lecture 1.5 — Three types of learning [Neural Networks for Machine Learning

07:38

[06]Lecture 2.1 — Types of neural network architectures [Neural Networks for Ma

07:29

[07]Lecture 2.2 — Perceptrons_ first-generation neural networks [Neural Network

08:17

[08]Lecture 2.3 — A geometrical view of perceptrons [Neural Networks for Machin

06:25

[09]Lecture 2.4 — Why the learning works [Neural Networks for Machine Learning]

05:10

[10]Lecture 2.5 — What perceptrons cant do [Neural Networks for Machine Learning

14:35

[11]Lecture 3.1 — Learning the weights of a linear neuron [Neural Networks for

11:56

[12]Lecture 3.2 — The error surface for a linear neuron [Neural Networks for Ma

05:04

[13]Lecture 3.3 — Learning weights of logistic output neuron [Neural Networks fo

03:57

[14]Lecture 3.4 — The backpropagation algorithm [Neural Networks for Machine Le

11:52

[15]Lecture 3.5 — Using the derivatives from backpropagation [Neural Networks f

09:50

[16]Lecture 4.1 — Learning to predict the next word [Neural Networks for Machin

12:34

[17]Lecture 4.2 — A brief diversion into cognitive science [Neural Networks for

04:27

[18]Lecture 4.3 — The softmax output function [Neural Networks for Machine Lear

07:21

[19]Lecture 4.4 — Neuro-probabilistic language models [Neural Networks for Mach

07:53

[20]Lecture 4.5 — Dealing with many possible outputs [Neural Networks for Machi

12:17

[21]Lecture 5.1 — Why object recognition is difficult [Neural Networks for Mach

04:41

[22]Lecture 5.2 — Achieving viewpoint invariance [Neural Networks for Machine L

05:59

[23]Lecture 5.3 — Convolutional nets for digit recognition [Neural Networks for

16:02

[24]Lecture 5.4 — Convolutional nets for object recognition [Neural Networks fo

17:44

[25]Lecture 6.1 — Overview of mini batch gradient descent [Neural Networks for

08:23

[26]Lecture 6.2 — A bag of tricks for mini batch gradient descent [Neural Networ

13:16

[27]Lecture 6.3 — The momentum method [Neural Networks for Machine Learning].zh

08:43

[28]Lecture 6.4 — Adaptive learning rates for each connection [Neural Networks

05:45

[29]Lecture 6.5 — Rmsprop_ normalize the gradient [Neural Networks for Machine L

11:39

[30]Lecture 7.1 — Modeling sequences_ a brief overview [Neural Networks for Mac

17:24

[31]Lecture 7.2 — Training RNNs with back propagation [Neural Networks for Mach

06:24

[32]Lecture 7.3 — A toy example of training an RNN [Neural Networks for Machine

06:15

[33]Lecture 7.4 — Why it is difficult to train an RNN_ [Neural Networks for Mac

07:44

[34]Lecture 7.5 — Long term Short term memory [Neural Networks for Machine Learn

09:16

[35]Lecture 8.1 — A brief overview of Hessian-free optimization [Neural Network

14:25

[36]Lecture 8.2 — Modeling character strings [Neural Networks for Machine Learni

14:36

[37]Lecture 8.3 — Predicting the next character using HF [Neural Networks for M

12:25

[38]Lecture 8.4 — Echo State Networks [Neural Networks for Machine Learning].zh

09:38

[39]Lecture 9.1 — Overview of ways to improve generalization [Neural Networks f

11:45

[40]Lecture 9.2 — Limiting the size of the weights [Neural Networks for Machine

06:23

[41]Lecture 9.3 — Using noise as a regularizer [Neural Networks for Machine Lea

07:32

[42]Lecture 9.4 — Introduction to the full Bayesian approach [Neural Networks f

10:50

[43]Lecture 9.5 — The Bayesian interpretation of weight decay [Neural Networks

10:53

[44]Lecture 9.6 — MacKay s quick and dirty method [Neural Networks for Machine

03:32

[45]10.1 — Why it helps to combine models [Neural Networks for Machine Learning

13:11

[46]Lecture 10.2 — Mixtures of Experts [Neural Networks for Machine Learning].z

13:16

[47]Lecture 10.3 — The idea of full Bayesian learning [Neural Networks for Mach

07:28

[48]Lecture 10.4 — Making full Bayesian learning practical [Neural Networks for

06:45

[49]Lecture 10.5 — Dropout [Neural Networks for Machine Learning].zh_en

08:36

[50]Lecture 11.1 — Hopfield Nets [Neural Networks for Machine Learning].zh_en

13:02

[51]Lecture 11.2 — Dealing with spurious minima [Neural Networks for Machine Le

11:03

[52]Lecture 11.3 — Hopfield nets with hidden units [Neural Networks for Machine

09:40

[53]Lecture 11.4 — Using stochastic units to improve search [Neural Networks fo

10:25

[54]Lecture 11.5 — How a Boltzmann machine models data [Neural Networks for Mac

11:45

[55]Lecture 12.1 — Boltzmann machine learning [Neural Networks for Machine Lear

12:16

[56]Lecture 12.2 — More efficient ways to get the statistics [Neural Networks f

14:49

[57]Lecture 12.3 — Restricted Boltzmann Machines [Neural Networks for Machine L

10:55

[58]Lecture 12.4 — An example of RBM learning [Neural Networks for Machine Lear

07:15

[59]Lecture 12.5 — RBMs for collaborative filtering [Neural Networks for Machin

08:17

[60]Lecture 13.1 — The ups and downs of backpropagation [Neural Networks for Ma

09:54

[61]Lecture 13.2 — Belief Nets [Neural Networks for Machine Learning].zh_en

12:36

[62]Lecture 13.3 — Learning sigmoid belief nets [Neural Networks for Machine Le

11:26

[63]Lecture 13.4 — The wake sleep algorithm [Neural Networks for Machine Learni

13:15

[64]Lecture 14.1 — Learning layers of features by stacking RBMs [Neural Network

17:35

[65]Lecture 14.2 — Discriminative learning for DBNs [Neural Networks for Machin

09:41

[66]Lecture 14.3 — Discriminative fine tuning [Neural Networks for Machine Lear

08:40

[67]Lecture 14.4 — Modeling real valued data with an RBM [Neural Networks for M

09:57

[68]Lecture 14.5 — RBMs are infinite sigmoid belief nets [Neural Networks for M

17:12

[69]Lecture 15.1 — From PCA to autoencoders [Neural Networks for Machine Learni

07:58

[70]Lecture 15.2 — Deep autoencoders [Neural Networks for Machine Learning].zh_

04:11

[71]Lecture 15.3 — Deep autoencoders for document retrieval [Neural Networks fo

08:20

[72]Lecture 15.4 — Semantic Hashing [Neural Networks for Machine Learning].zh_e

08:51

[73]Lecture 15.5 — Learning binary codes for image retrieval [Neural Networks f

09:38

[74]Lecture 15.6 — Shallow autoencoders for pre-training [Neural Networks for M

07:03

[75]Lecture 16.1 — Learning a joint model of images and captions [Neural Networ

09:05

[76]Lecture 16.2 — Hierarchical Coordinate Frames [Neural Networks for Machine

09:41

[77]Lecture 16.3 — Bayesian optimization of hyper-parameters [Neural Networks f

13:31

[78]Lecture 16.4 — The fog of progress [Neural Networks for Machine Learning].z

02:25

CMU《机器学习实践|17-691 Machine Learning in Practice 2023》中英字幕

CMU《机器学习导论（研究生）|10-701 Introduction to Machine Learning 2020》中英字幕

UCB《计算机视觉与计算摄影学|CS 194-26 Computer Vision and Computational Photography 2022》中英字

斯坦福大学《深度强化学习|CS224R Deep Reinforcement Learning Spring 2025》中英字幕

斯坦福大学《用于视觉识别的卷积神经网络2017春|cs231n CNN for Visual Recognition 2017sp》

GPU MODE《CUDA、GPU编程|GPU MODE》中英字幕（deepseek-v3.2

[20250426]Lecture 54_ Small RL Models at the Speed of Light with LeanRL.zh_en

53:49

[20250502]Lecture 55_ Modular’s unified device accelerator language.zh_en

02:13:12

[20250509]Lecture 56_ Kernel Benchmarking Tales.zh_en

01:07:57

[20250517]Lecture 57_ CuTe.zh_en

01:24:33

[20250526]Lecture 58_ Disaggregated LLM Inference.zh_en

01:15:19

[20250531]Lecture 59_ FastVideo.zh_en

01:04:11

[20250607]Lecture 60_ Optimizing Linear Attention.zh_en

01:07:39

[20250614]Lecture 61_ D-Matrix Corsair.zh_en

01:38:09

[20250628]Lecture 62_ Exo 2 Growing a scheduling language.zh_en

01:08:23

[20250712]Lecture 63_ Search-Based Deep Learning Compilers.zh_en

01:09:19

[20250724]Lecture 64_ Multi-GPU programming.zh_en

01:15:23

[20250726]Lecture 65_ Neighborhood Attention.zh_en

01:37:53

[20250806]Lecture 66_ Game Arena.zh_en

53:33

[20250816]Lecture 67_ NCCL and NVSHMEM.zh_en

01:40:43

[20250823]Lecture 68_ Landscape of GPU Centric communication.zh_en

01:00:07

[20250823]Lecture 69_ Quartet 4 bit training.zh_en

01:09:11

[20250830]Lecture 70_ PCCL Fault tolerant collectives.zh_en

01:05:23

[20250831]Lecture 71_ [ScaleML Series] FlexOlmo_ Open Language Models for Flexib

01:24:51

[20250831]Lecture 72_ [ScaleML Series] Efficient & Effective Long-Context Modeli

55:18

[20250831]Lecture 73_ [ScaleML Series] Quantization in Large Models.zh_en

01:18:26

[20250831]Lecture 74_ [ScaleML Series] Positional Encodings and PaTH Attention.z

01:40:32

[20250831]Lecture 75 [ScaleML Series] GPU Programming Fundamentals + ThunderKitt

02:39:22

[20250910]Lecture 76_ BackendBench fixing the LLM kernel correctness problem.zh_

17:54

[20250910]Lecture 77_ Domain Specific Languages for GPU Kernels.zh_en

23:39

[20250913]Lecture 78 Iris_ Multi-GPU Programming in Triton.zh_en

01:16:38

[20250914]Lecture 79 Mirage (MPK)_ Compiling LLMs into Mega Kernels.zh_en

01:12:52

[20251001]Lecture 80_ How FlashAttention 4 Works.zh_en

01:15:09

[20251004]Lecture 81_ High-performance purely functional data-parallel array pro

01:11:03

[20251017]Lecture 82 Helion_ A high-level DSL for ML kernels.zh_en

01:06:20

[20251018]Lecture 83_ Formalized Kernel Derivation.zh_en

01:40:06

20251115-Lecture 84_ Numerics and AI.zh_en

02:39:09

20251206-Lecture 85_ Factorio Learning Environment.zh_en

01:06:12

20251207-Lecture 86_ Getting Started with CuTe DSL.zh_en

01:13:19

20251207-Lecture 87_ Low Latency Communication Kernels with NVSHMEM.zh_en

01:07:07

[20251220]Lecture 88_ TinyTPU.zh_en

54:32

[20251220]Lecture 89_ cuTile (from friends at NVIDIA).zh_en

01:43:31

[20260110]Lecture 90_ Building resilient ML Engineering skills.zh_en

01:54:07

[20260115]Mega Lecture 91_ Reinforcement Learning, Agents & OpenEnv.zh_en

03:21:51

[20260117]Lecture 92_ Smol Training Playbook.zh_en

01:26:03

[20260124]Lecture 93_ Cornserve Easy, Fast and Scalable Multimodal AI.zh_en

44:19

[20260131]Lecture 94_ tvm-ffi.zh_en

01:28:31

[20260207]Lecture 95_ Single controller programming with Monarch.zh_en

02:02:24

[20260212]Lecture 96_ TLX.zh_en

01:29:58

[20260219]Lecture 97_ HipKittens.zh_en

01:25:05

[20260225]Lecture 98_ GPU Observability.zh_en

46:53

[20260228]Lecture 99_ Distributed ML on consumer devices.zh_en

01:05:53

[20260304]Lecture 100_ InferenceX Continuous OSS Inference Benchmarking.zh_en

01:12:06

[20260321]Lecture 101_ Learning CUTLASS the hard way.zh_en

01:11:17

[20260414]Lecture 102_ quartet v2.zh_en

01:47:17

[20260418]Lecture 103_ Fundamentals of CuTe Layout Algebra and Category-theoreti

02:33:57

[20260422]Lecture 104_ Gluon and Linear Layouts.zh_en

01:57:57

[20260502]Lecture 105_ cuDNN mxfp8 attention-HcnybHRbTcc.zh_en

01:00:18

《人工智能|6.034 Artificial Intelligence, Fall 2010》中英字幕

杜克大学《机器学习|Introduction to Machine Learning》中英字幕

《动手深度学习|15.773 Hands-On Deep Learning Spring 2024》中英字幕

杜克大学《可解释人工智能|Explainable AI (XAI)》中英字幕

UCLA《模式识别和机器学习|Introduction to Pattern Recognition and ML Winter 2024》中英字幕

华盛顿大学《基于数据的机器学习控制|Data-Driven Control with Machine Learning 2018》中英字幕

康奈尔大学《智能系统的机器学习|CS4780 Machine Learning for Intelligent Systems 2017》中英字幕

[01]Lecture 1 ＂Supervised Learning Setup＂ -Cornell CS4780 Machine Learning for D

47:49

[02]Lecture 2 ＂Supervised Learning Setup Continued＂ -Cornell CS4780 SP17.zh_en

48:45

[03]Lecture 3 ＂k-nearest neighbors＂ -Cornell CS4780 SP17.zh_en

49:42

[04]Lecture 4 ＂Curse of Dimensionality _ Perceptron＂ -Cornell CS4780 SP17.zh_en

47:43

[05]Lecture 5 ＂Perceptron＂ -Cornell CS4780 SP17.zh_en

49:57

[06]Lecture 6 ＂Perceptron Convergence Proof＂ -Cornell CS4780 SP17.zh_en

49:26

[07]Lecture 7 ＂Estimating Probabilities from Data_ Maximum Likelihood Estimation

48:50

[08]Machine Learning Lecture 8 ＂Estimating Probabilities from Data_ Naive Bayes＂

44:28

[09]Machine Learning Lecture 9 ＂Naive Bayes continued＂ -Cornell CS4780 SP17.zh_e

38:52

[10]Machine Learning Lecture 10 ＂Naive Bayes continued＂ -Cornell CS4780 SP17.zh_

48:33

[11]Machine Learning Lecture 11 ＂Logistic Regression＂ -Cornell CS4780 SP17.zh_en

47:39

[12]Machine Learning Lecture 12 ＂Gradient Descent _ Newtons Method＂ -Cornell CS4

49:19

[13]Machine Learning Lecture 13 ＂Linear _ Ridge Regression＂ -Cornell CS4780 SP17

39:50

[14]Machine Learning Lecture 14 ＂(Linear) Support Vector Machines＂ -Cornell CS47

49:59

[15]Machine Learning Lecture 15 ＂(Linear) Support Vector Machines continued＂ -Co

50:04

[16]Machine Learning Lecture 16 ＂Empirical Risk Minimization＂ -Cornell CS4780 SP

46:33

[17]Machine Learning Lecture 17 ＂Regularization _ Review＂ -Cornell CS4780 SP17.z

52:30

[18]Machine Learning Lecture 18 ＂Review Lecture II＂ -Cornell CS4780 SP17.zh_en

50:48

[19]Machine Learning Lecture 19 ＂Bias Variance Decomposition＂ -Cornell CS4780 SP

51:13

[20]Machine Learning Lecture 20 ＂Model Selection _ Regularization _ Overfitting＂

47:02

[21]Machine Learning Lecture 21 ＂Model Selection _ Kernels＂ -Cornell CS4780 SP17

47:36

[22]Machine Learning Lecture 22 ＂More on Kernels＂ -Cornell CS4780 SP17.zh_en

51:54

[23]Machine Learning Lecture 23 ＂Kernels Continued Continued＂ -Cornell CS4780 SP

43:42

[24]Machine Learning Lecture 24 ＂Kernel Support Vector Machine＂ -Cornell CS4780

50:07

[25]Machine Learning Lecture 25 ＂Kernelized algorithms＂ -Cornell CS4780 SP17.zh_

49:28

[26]Machine Learning Lecture 26 ＂Gaussian Processes＂ -Cornell CS4780 SP17.zh_en

52:42

[27]Machine Learning Lecture 27 ＂Gaussian Processes II _ KD-Trees _ Ball-Trees＂

50:53

[28]Machine Learning Lecture 28 ＂Ball Trees _ Decision Trees＂ -Cornell CS4780 SP

48:59

[29]Machine Learning Lecture 29 ＂Decision Trees _ Regression Trees＂ -Cornell CS4

50:23

[30]Machine Learning Lecture 30 ＂Bagging＂ -Cornell CS4780 SP17.zh_en

49:43

[31]Machine Learning Lecture 31 ＂Random Forests _ Bagging＂ -Cornell CS4780 SP17.

47:25

[32]Machine Learning Lecture 32 ＂Boosting＂ -Cornell CS4780 SP17.zh_en

48:28

[33]Machine Learning Lecture 33 ＂Boosting Continued＂ -Cornell CS4780 SP17.zh_en

47:54

[34]Machine Learning Lecture 34 ＂Boosting _ Adaboost＂ -Cornell CS4780 SP17.zh_en

48:41

[35]Machine Learning Lecture 35 ＂Neural Networks _ Deep Learning＂ -Cornell CS478

49:39

[36]Machine Learning Lecture 36 ＂Neural Networks _ Deep Learning Continued＂ -Cor

51:57

[37]Machine Learning Lecture 37 ＂Neural Networks _ Deep Learning＂ -Cornell CS478

51:00

[38]In-class Kaggle Competition in less than 5 Minutes.zh_en

08:56

[39]Machine Learning Certificate from Cornell University.zh_en

01:16

[40]CS4780 Transformers (additional lecture 2023).zh_en

57:28

[41]The DBSCAN Clustering Algorithm Explained.zh_en

08:28

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