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【深度强化学习 CS285 2023】伯克利—中英字幕

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P27 Cornell CS 5787： Applied Machine Learning. Lecture 6. Part 5： Softmax Regres

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P50 Cornell CS 5787： Applied Machine Learning. Lecture 13. Part 2： Additive Mode

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P51 Cornell CS 5787： Applied Machine Learning. Lecture 13. Part 3： Gradient Boos

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P57 Cornell CS 5787： Applied Machine Learning. Lecture 15. Part 3： Convolutional

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P58 Cornell CS 5787： Applied Machine Learning. Lecture 16. Part 1： Introduction

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P63 Cornell CS 5787： Applied Machine Learning. Lecture 17. Part 3： Latent Variab

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P64 Cornell CS 5787： Applied Machine Learning. Lecture 18. Part 1： Gaussian Mixt

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P65 Cornell CS 5787： Applied Machine Learning. Lecture 18. Part 2： Expectation M

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P66 Applied Machine Learning. Lecture 18. Part 3： Expectation Maximization in Ga

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P67 Applied Machine Learning. Lecture 18. Part 4： Generalization in Probabilisti

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P68 Cornell CS 5787： Applied Machine Learning. Lecture 19. Part 1： What is Dimen

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P69 Cornell CS 5787： Applied Machine Learning. Lecture 19. Part 3： Principal Com

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P70 Cornell CS 5787： Applied Machine Learning. Lecture 20. Part 1： Machine Learn

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P71 Cornell CS 5787： Applied Machine Learning. Lecture 20. Part 2： Evaluating Cl

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P72 Cornell CS 5787： Applied Machine Learning. Lecture 20. Part 3： Advanced Clas

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P73 Cornell CS 5787： Applied Machine Learning. Lecture 20. Part 4： Evaluating Re

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P74 Cornell CS 5787： Applied Machine Learning. Lecture 21. Part 1： Error Analysi

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P76 Cornell CS 5787： Applied Machine Learning. Lecture 21. Part 3： Baselines

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P77 Cornell CS 5787： Applied Machine Learning. Lecture 22. Part 1： Learning Curv

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P78 Cornell CS 5787： Applied Machine Learning. Lecture 22. Part 2： Loss Curves

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P79 Cornell CS 5787： Applied Machine Learning. Lecture 22. Part 3： Validation Cu

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P80 Cornell CS 5787： Applied Machine Learning. Lecture 22. Part 4： Distribution

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P15 Statistical Machine Learning Part 13 - Linear discriminant analysis

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P16 Statistical Machine Learning Part 14 - Logistic regression

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P45 Statistical Machine Learning Part 42 - Statistical learning theory： Rademach

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P47 Statistical Machine Learning Part 44 - Statistical learning theory： Revisiti

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P48 Statistical Machine Learning Part 45 - ML and Society： The general debate

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P52 Statistical Machine Learning Part 49 - ML and Society： Explainable ML

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P53 Statistical Machine Learning Part 50 - ML and Society： The energy footprint

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P54 Statistical Machine Learning Part 51 - Low rank matrix completion： algorithm

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P55 Statistical Machine Learning Part 52 - Low rank matrix completion： theory

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P56 Statistical Machine Learning Part 53 - Compressed sensing

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P57 Statistical Machine Learning Part 54 - ML pipeline： data, preprocessing, lea

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P58 Statistical Machine Learning Part 55 - ML pipeline： evaluation

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P17 RAG Application using Langchain Mistral AI and Weviate db #llm #rag #langcha

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P18 RAG Application Using OpenSource Framework @LlamaIndex and @Mistral-AI #rag

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P36 Realtime Powerful RAG Pipeline using Neo4j(Knowledge Graph Db) and Langchain

53:17

P37 Advance RAG 01 - Powerful RAG Using Hybrid Search(Keyword+vVector search) ｜

01:01:10

P38 Advacne RAG 02 - Hybrid Search (Keyword + Vector ) & Reranking With Cohere

48:16

P39 End-to-End Weather Chatbot with Google DialogFlow and AWS CI⧸CD Deployment

02:35:31

P40 Advanced RAG 03 - Reranking with Sentence Transformers and BM25 API

28:43

P41 Advanced RAG 04 - Reranking with Cross Encoders, and Cohere API

19:21

P42 Advance RAG 05 - Merger Retriever and LongContextReorder ｜ Lost in Middle Ph

40:11

P43 Advance RAG 06- RAG Fusion (Get More Relevant Results for Your RAG) ｜ Rerank

48:59

P44 Advance RAG 07 - Flash Reranker for Superfast Reranking

29:48

P45 Advance RAG 08- Powerful RAG with Langchain Contextual Compression Retrieve

57:20

P46 Advance RAG 09- Powerful RAG with Self Querying Retriever #ai #llm #openai

45:11

P47 Advance RAG 10- Powerful RAG with Parent Document Retriever #ai #llm #opena

51:05

P48 End-to-End RAG With Llama 3.1, Langchain, FAISS and OLlama #ai #llm #llama #

38:04

P49 Advance RAG 11- Powerful RAG with Sentence Window Retriever using @LlamaInd

39:47

P50 Advance RAG 12- Powerful RAG with Merger Retriever and Hypothetical Documen

31:53

P51 Complete @LangChain Essential in 1 shot ｜ LangChain Core ｜ LangServe ｜ LangG

01:55:49

P52 Chatbot Using @LangChain With Memory(Chat History) ｜ LangChain Core ｜ LangSm

01:07:51

P53 RAG Based Chatbot With Memory(Chat History) ｜ Creating History Aware Retriev

37:27

P54 Langchain Conversation Buffer Memory vs Conversation Buffer Window Memory ｜

40:45

P55 Langchain Conversation Entity Memory ｜ Langchain Memory Class ｜ Chat Histor

33:00

P56 Langchain Conversation Summary Memory vs Conversation Summary Buffer Memory

44:12

P57 LangChain Expression language(LCEL) for Chaining the Components ｜ All Runnab

52:49

P58 LangGraph 01： Syllabus Introduction of End to End LangGraph Course ｜ LangCha

10:35

P59 LangGraph：02 LangGraph Course Pre-requist ｜ AI Assistant ｜ RAG I LCEL ｜ Tool

42:42

P60 LangGraph：03 LangChain AI Agents ｜ Tools ｜ Tool Calling Agent ｜ ReAct Agents

39:15

P61 LangGraph：04 LangChain ReAct Agent with Custom Tool and Self-Ask Agent with

52:15

P62 LangGraph：05 Building AI Agent from Scratch Using Python with Custom Tool #l

45:31

P63 LangGraph：06 Detailed Introduction of LangGraph #llm #genai #ai #aiagents

19:49

P64 LangGraph：07 Code LangGraph From Scratch ｜ LangGraph Deep Dive #llm #genai #

27:10

P65 LangGraph：08 Adding RAG to LangGraph Workflow ｜ LangGraph Deep Dive #llm #ge

37:50

P66 LangGraph：09 End to End Chatbot using LangGraph With Memory #llm #genai #aia

46:04

P67 🟥 100 + Generative AI Interview Questions and Answers Discussion

01:17:41

P68 LangGraph：10 Structured Output with LangGraph Agents #llm #genai #aiagents #

26:19

P69 LangGraph：11 Building Finance Bot with LangGraph's ReAct Agent #llm #genai #

30:54

P70 LangGraph：12 LangGraph Agent with Human-In-The-Loop, Checkpoints & Breakpoin

44:22

P71 LangGraph：13 Corrective RAG for Real Time AI Application #llm #genai #aiagen

42:36

P72 LangGraph：14 Agentic RAG for Real Time Agentic AI Application #llm #genai #a

01:06:11

P73 LangGraph：15 Self-RAG for Real Time Agentic AI Application #llm #genai #aiag

59:34

P74 Roadmap of Agentic AI & Generative AI with 150 + Interview Questions and Ans

17:36

P75 LangGraph：16 Advance SQL Database Agent Powered by LangGraph #llm #genai #ai

37:08

P76 🟥 Autogen Research Agent： End-to-End Project for Paper Analysis & Summariza

01:35:31

P77 LangGraph：17 Introduction to Multi-Agent System #llm #genai #aiagents #ai #g

01:07:11

P78 LangGraph：18 Network or Collaborative Multi-Agent System Implementation #aia

52:38

P79 LangGraph：19 Research and Analysis with Collaborative Multi-Agent System #ai

36:44

P80 LangGraph：20 Supervisor Multi-Agentic System ｜ Agentic AI #aiagents #ai #gen

45:54

P81 LangGraph：21 End-to-End Supervisor Multi-Agentic AI Project for Booking Doct

01:09:21

P82 LLM Fine-Tuning： 01 LLM Fine-Tuning From Scratch—Full Playlist Coming Your W

20:01

P83 LLM Fine-Tuning： 02 Understanding Model Pretraining and Training in AI #aia

01:04:41

P84 🟥 Live Q&A on Generative & Agentic AI—Ask Me Anything!

37:46

图宾根大学【中英⚡机器学习数学|2020 Tübingen Mathematics for Machine Learning】

卡耐基梅隆大学【中英⚡深度学习导论|11785 2025 Introduction to Deep Learning】

斯坦福大学【中英⚡基于人类偏好的机器学习|CS329H 2024 Machine Learning from Human Preferences】

卡耐基梅隆大学【中英⚡机器学习导论|10-310 2020 Intro to Machine Learning Spring】

卡耐基梅隆大学【中英⚡机器学习|10-601 2015 Machine Learning】

佐治亚理工【中英⚡强化学习导论|CS8803 Introduction to Reinforcement Learning】

苏黎世联邦理工【中英⚡科学与工程中的人工智能|2024 AI in the Sciences and Engineering】

麻省理工学院【中英⚡万物皆可AI|2025 How to AI (Almost) Anything】

IBM【中英⚡面向项目经理的生成式AI 专项课程|Generative AI for Project Managers Specialization】

加州大学伯克利分校【中英⚡机器学习导论|CS189 2025 Introduction to Machine Learning】

Lecture 1 (January 22): Introduction. Classification. Training, validation, and

01:23:00

Lecture 2 (January 27): Linear classifiers. Decision functions and decision boun

01:18:10

Lecture 3 (January 29): Gradient descent, stochastic gradient descent, and the p

01:23:00

Lecture 4 (February 3): The support vector classifier, aka soft-margin support v

01:23:00

Lecture 5 (February 5): Machine learning abstractions: application/data, model,

01:23:00

Lecture 6 (February 10): Decision theory, also known as risk minimization: the B

01:23:00

Lecture 7 (February 12): Gaussian discriminant analysis, including quadratic dis

01:23:00

Lecture 8 (February 19): Eigenvectors, eigenvalues, and the eigendecomposition o

01:23:00

Lecture 9 (February 24): MLE, QDA, and LDA revisited for anisotropic Gaussians.

01:23:00

Lecture 10 (February 26): Regression: fitting curves to data. The 3-choice menu

01:23:00

Lecture 11 (March 3): Newton's method and its application to logistic regression

01:23:00

Lecture 12 (March 5): Statistical justifications for regression. The empirical d

01:23:00

Lecture 13 (March 10): Ridge regression: penalized least-squares regression for

01:09:25

Lecture 14 (March 12): Decision trees; algorithms for building them. Entropy and

01:23:00

Lecture 15 (March 19): More decision trees: decision tree regression; stopping e

01:23:00

Lecture 16 (March 31): Neural networks. Gradient descent and the backpropagation

01:23:00

Lecture 17 (April 2): The vanishing gradient problem. Rectified linear units (Re

01:23:00

Lecture 18 (April 7): Neuron biology: axons, dendrites, synapses, action potenti

01:23:00

Lecture 19 (April 9): Convolutional neural networks. Neurology of retinal gangli

01:23:00

Lecture 20 (April 14): Unsupervised learning. Principal components analysis (PCA

01:23:00

Lecture 21 (April 16): The singular value decomposition (SVD) and its applicatio

01:23:00

Lecture 22 (April 21): The pseudoinverse and its relationship to the singular va

01:23:00

Lecture 23 (April 23): Batch normalization. Residual neural networks (“ResNets”)

01:23:00

Lecture 24 (April 28): AdaBoost, a boosting method for ensemble learning

01:23:00

Lecture 25 (April 30): The exhaustive algorithm for k-nearest neighbor queries.

01:22:59

马里兰大学【中英⚡深度学习基础|CMSC828W 2020 Foundations of Deep Learning】

P1 Lecture 1 - Deep Learning Foundations： review of basic DL models and optimiza

01:15:14

P2 Lecture 2 - Deep Learning Foundations： the role of over parameterization in

01:15:35

P3 Lecture 3 - Deep Learning Foundations： the role of over parameterization in D

01:13:30

P4 Lecture 4 - Deep Learning Foundations： the implicit bias of gradient descent

01:15:01

P5 Lecture 5 - Deep Learning Foundations： deep learning generalization

01:15:38

P6 Lecture 6 - Deep Learning Foundations： deep learning generalization (part II)

01:13:05

P7 Lecture 7 - Deep Learning Foundations： Neural Tangent Kernels

01:14:52

P8 Lecture 8 - Deep Learning Foundations： Adversarial Robustness： Formulations,

01:02:32

P9 Lecture 9 - Deep Learning Foundations by Soheil Feizi： Are Adversarial Exampl

01:10:50

P10 Lecture 10-Deep Learning Foundations by Soheil Feizi：Provable & Generalizabl

01:12:37

P11 Lecture 11 - Deep Learning Foundations by Soheil Feizi ： Poisoning Attacks a

01:15:09

P12 Lecture 12 - Deep Learning Foundations by Soheil Feizi ： Variational AutoEnc

01:16:38

P13 Lecture 13 - Deep Learning Foundations by Soheil Feizi ： Generative Adversar

01:12:53

P14 Lecture 14 - Deep Learning Foundations by Soheil Feizi ： Min-Max Optimizatio

01:08:58

P15 Lecture 15 - Deep Learning Foundations by Soheil Feizi ： Min-Max Optimizatio

01:14:21

P16 Lecture 16 - Deep Learning Foundations by Soheil Feizi ： Flow-based Generati

01:16:12

P17 Lecture 17 - Deep Learning Foundations by Soheil Feizi ： Domain Adaptation (

01:08:42

P18 Lecture 18 - Deep Learning Foundations by Soheil Feizi ： Domain Adaptation (

01:11:32

P19 Lecture 20 - Deep Learning Foundations by Soheil Feizi ： Domain Generalizati

01:09:32

P20 Lecture 21 - Deep Learning Foundations by Soheil Feizi ： Self-Supervised⧸Con

01:05:52

P21 Lecture 22 - Deep Learning Foundations by Soheil Feizi ： Self-Supervised Lea

01:13:00

P22 Lecture 23 - Deep Learning Foundations by Soheil Feizi ： Meta Learning

01:04:52

P23 Lecture 24 - Deep Learning Foundations, Guest Lecture by Aya Ismail： RNNs, L

01:04:20

P24 Lecture 25 - Deep Learning Foundations, Guest Lecture by Aya Ismail： Deep Le

01:05:42

P25 Lecture 27 - Deep Learning Foundations by Soheil Feizi ： Reinforcement Learn

01:06:17

P26 Lecture 28 - Deep Learning Foundations by Soheil Feizi ： Reinforcement Learn

01:09:18

P27 Lecture 29 - Deep Learning Foundations by Soheil Feizi ： Reinforcement Learn

01:09:23

P28 Deep Learning Foundations by Soheil Feizi ： Course Summary

05:55

P29 Deep Learning Foundations： Misha Belkin's Talk on deep learning through the

01:13:47

P30 Deep Learning Foundations： Andrew Wilson's Talk on How Do We Build Models Th

01:15:47

P31 Deep Learning Foundations： Simon Du's Talk on Passive and Active Multi-Task

01:06:16

P32 Deep Learning Foundations： Balaji Lakshminarayanan's Talk on Reliability via

01:08:59

P33 Deep Learning Foundations： Daniel Roy and Mufan Li's Talk on ＂Neural Covaria

59:51

P34 Deep Learning Foundations： Jonathan Frankle talk on Faster Neural Network Tr

01:11:40

P35 Deep Learning Foundations： Mahdi Soltanolkotabi's talk on Feature learning &

01:15:58

P36 Deep Learning Foundations： Constantinos Daskalakis's talk on Equilibrium Com

01:20:26

P37 Deep Learning Foundations： Xinyun Chen 's talk on ＂Learning-Based Program Sy

01:19:12

P38 Deep Learning Foundations： Dorsa Sadigh 's talk on ＂Learning Robot Policies＂

01:10:02

P39 Deep Learning Foundations： Arash Vahdat's talk on ＂Denoising Diffusion Model

01:17:46

P40 Deep Learning Foundations by Soheil Feizi ： Transformers

01:28:55

P41 Deep Learning Foundations by Soheil Feizi ： Linear Attention

23:03

P42 Deep Learning Foundations by Soheil Feizi ： Vision Transformers

39:49

P43 Deep Learning Foundations by Soheil Feizi ： Hierarchical Vision Transformers

34:52

P44 Deep Learning Foundations by Soheil Feizi ： Diffusion Models

02:58:10

P45 Deep Learning Foundations by Soheil Feizi ： Diffusion Models： A Score Matchi

46:23

P46 The dangers posed by AI and disinformation during elections

01:01:26

P47 Deep Learning Foundations by Soheil Feizi ： Latent Text-to-Image Diffusion M

30:44

P48 Deep Learning Foundations by Soheil Feizi ： Large Language Models

03:47:26

P49 Deep Learning Foundations by Soheil Feizi ： Large Language Models, Part II

01:27:52

卡耐基梅隆大学【中英⚡最优控制|2025 16-745 Optimal Control】

卡耐基梅隆大学【中英⚡深度学习导论|11-785 2025 Introduction to Deep Learning】

卡耐基梅隆【中英⚡高级机器学习|10-715 2015 Advanced Introduction to Machine Learning】

斯坦福大学【中英⚡Transformer与LLMs|CME295 2025 Transformers & Large Language Models】

Valerio Velardo【中英⚡面向机器学习的音频信号处理 Audio Signal Processing for Machine Learning】

加利福尼亚大学洛杉矶分校【中英⚡模式识别与机器学习导论|STATSC161 2023 Introduction to Pattern Recognition 】

卡耐基梅隆大学【中英⚡结构化机器学习|10-418 2019 Machine Learning for Structured Data】

华盛顿大学【中英⚡机器学习|CSEP546 2019 Machine Learning】

AmirhosseinKazemnejad【中英⚡强化学习与LLM|nanoAhaMoment: RL for LLM from Scratch】

麻省理工学院【中英⚡应用统计学|18.650 2016 Statistics for Applications】

阿尔伯塔大学【中英⚡强化学习|Reinforcement Learning Specialization】

华盛顿大学【中英⚡机器学习|UW CSE 546 Machine Learning】

加州大学伯克利分校【中英⚡数据，推理与决策II |DATA102 2021 Data, Inference, and Decisions】

加州大学伯克利分校【中英⚡数据，推理与决策I|data102 2021 Data, Inference, and Decisions】

纽约石溪大学【中英⚡数据科学|CSE519 2024 Data Science Fundamentals】

纽约大学【中英⚡金融领域的机器学习和强化学习|Machine Learning and Reinforcement Learning in Finance S】

斯坦福大学【中英⚡深度学习自然语言处理|2024 CS224N Natural Language Processing with Deep Learning】

P1 NLP with Deep Learning ｜ Spring 2024 ｜ Lecture 1 - Intro and Word Vectors

01:20:17

P2 NLP with Deep Learning ｜ Spring 2024 ｜ Lecture 2 - Word Vectors and Language

01:19:12

P3 NLP with Deep Learning ｜ Spring 2024 ｜ Lecture 3 - Backpropagation, Neural Ne

01:13:27

P4 NLP with Deep Learning ｜ Spring 2024 ｜ Lecture 4 - Dependency Parsing

01:18:57

P5 NLP with Deep Learning ｜ Spring 2024 ｜ Lecture 5 - Recurrent Neural Networks

01:18:52

P6 NLP with Deep Learning ｜ Spring 2024 ｜ Lecture 6 - Sequence to Sequence Model

01:17:09

P7 NLP w⧸ DL ｜ Spring 2024 ｜ Lecture 7 - Attention, Final Projects and LLM Intro

01:17:44

P8 NLP with Deep Learning ｜ 2023 ｜ Lecture 8 - Self-Attention and Transformers

01:17:04

P9 NLP with Deep Learning ｜ 2023 ｜ Lecture 9 - Pretraining

01:18:46

P10 NLP with Deep Learning ｜ 2023 ｜ Lecture 11 - Natural Language Generation

01:18:25

P11 NLP with Deep Learning ｜ Spring 2024 ｜ Lecture 10 - Post-training by Archit

01:19:42

P12 NLP with Deep Learning ｜ Spring 2024 ｜ Lecture 11 - Benchmarking by Yann Dub

01:24:24

P13 NLP w⧸ DL ｜ Spring 2024 ｜ Lecture 12 - Efficient Training, Shikhar Murty

01:02:32

P14 NLP w⧸ DL｜ Spring 2024 ｜ Lecture 13 - Brain-Computer Interfaces, Chaofei Fan

01:12:49

P15 NLP w⧸ DL ｜ Spring 2024 ｜ Lecture 14 - Reasoning and Agents by Shikhar Murty

01:03:42

P16 NLP with Deep Learning ｜ Spring 2024 ｜ Lecture 15 - After DPO by Nathan Lamb

01:08:57

P17 NLP with Deep Learning ｜ Spring 2024 ｜ Lecture 16 - ConvNets and TreeRNNs

01:11:56

P18 NLP with Deep Learning ｜ Spring 2024 ｜ Lecture 18 - NLP, Linguistics, Philos

01:16:27

P19 NLP with Deep Learning ｜ 2023 ｜ Lecture 16 - Multimodal Deep Learning, Douwe

01:18:23

P20 NLP with Deep Learning ｜ 2023 ｜ Lec. 19 - Model Interpretability & Editing,

01:11:42

P21 NLP with Deep Learning ｜ 2023 ｜ Python Tutorial, Manasi Sharma

47:14

P22 NLP with Deep Learning ｜ 2023 ｜ PyTorch Tutorial, Drew Kaul

47:01

P23 NLP with Deep Learning ｜ 2023 ｜ Hugging Face Tutorial, Eric Frankel

47:57

斯坦福大学【中英⚡计算机视觉中的深度学习|2025 CS231N Deep Learning for Computer Vision】

斯坦福大学【中英⚡ 强化学习|2024 CS234 I Reinforcement Learning】

斯坦福大学【中英⚡自然语言理解 |2021 CS224U Natural Language Understanding】

P1 Introduction and Welcome ｜ Stanford CS224U Natural Language Understanding ｜ S

03:02

P2 Course Overview ｜ Stanford CS224U Natural Language Understanding ｜ Spring 202

46:08

P3 Homework 1： Word Relatedness ｜ Stanford CS224U Natural Language Understanding

10:15

P4 High-level Goals & Guiding Hypotheses ｜ Stanford CS224U Natural Language Unde

08:35

P5 Matrix Designs ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2021

10:40

P6 Vector Comparison ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2

11:44

P7 Basic Reweighting ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2

16:41

P8 Dimensionality Reduction ｜ Stanford CS224U Natural Language Understanding ｜ S

26:21

P9 Retrofitting ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2021

05:24

P10 Static Representations ｜ Stanford CS224U Natural Language Understanding ｜ Sp

12:16

P11 Homework 2： Sentiment Analysis ｜ Stanford CS224U Natural Language Understand

13:57

P12 Sentiment Analysis ｜ Stanford CS224U Natural Language Understanding ｜ Spring

12:00

P13 General Practical Tips ｜ Stanford CS224U Natural Language Understanding ｜ Sp

15:21

P14 Stanford Sentiment Treebank ｜ Stanford CS224U Natural Language Understanding

08:16

P15 DynaSent ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2021

14:24

P16 sst.py ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2021

12:02

P17 Hyperparameter Search ｜ Stanford CS224U Natural Language Understanding ｜ Spr

10:12

P18 Feature Representation ｜ Stanford CS224U Natural Language Understanding ｜ Sp

15:44

P19 RNN Classifiers ｜ Stanford CS224U Natural Language Understanding ｜ Spring 20

09:19

P20 Contextual Representation Models ｜ Stanford CS224U Natural Language Understa

17:20

P21 Transformers ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2021

11:53

P22 BERT ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2021

09:49

P23 RoBERTa ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2021

06:32

P24 ELECTRA ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2021

07:23

P25 Practical Fine-tuning ｜ Stanford CS224U Natural Language Understanding ｜ Spr

07:39

P26 Homework 3： Colors ｜ Stanford CS224U Natural Language Understanding ｜ Spring

08:42

P27 Grounded Language Understanding ｜ Stanford CS224U Natural Language Understan

16:29

P28 Speakers ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2021

07:46

P29 Listeners ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2021

06:06

P30 Varieties of contextual grounding ｜ Stanford CS224U Natural Language Underst

06:45

P31 The Rational Speech Acts Model ｜ Stanford CS224U Natural Language Understand

09:49

P32 Neural RSA ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2021

08:11

P33 Natural Language Inference ｜ Stanford CS224U Natural Language Understanding

11:07

P34 SNLI, MultiNLI, and Adversarial NLI ｜ Stanford CS224U Natural Language Under

14:20

P35 Adversarial Testing ｜ Stanford CS224U Natural Language Understanding ｜ Sprin

08:51

P36 Modeling Strategies ｜ Stanford CS224U Natural Language Understanding ｜ Sprin

13:20

P37 Attention ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2021

10:03

P38 NLU and Information Retrieval ｜ Stanford CS224U Natural Language Understandi

20:02

P39 Classical IR ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2021

23:47

P40 Neural IR, part 1 ｜ Stanford CS224U Natural Language Understanding ｜ Spring

07:41

P41 Neural IR, part 2 ｜ Stanford CS224U Natural Language Understanding ｜ Spring

09:36

P42 Neural IR, part 3 ｜ Stanford CS224U Natural Language Understanding ｜ Spring

17:58

P43 Relation Extraction ｜ Stanford CS224U Natural Language Understanding ｜ Sprin

13:06

P44 Data Resources ｜ Stanford CS224U Natural Language Understanding ｜ Spring 202

15:17

P45 Problem Formulation ｜ Stanford CS224U Natural Language Understanding ｜ Sprin

14:48

P46 Evaluation ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2021

11:31

P47 Simple Baselines ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2

16:33

P48 Directions to Explore ｜ Stanford CS224U Natural Language Understanding ｜ Spr

20:23

P49 Overview of Analysis Methods in NLP ｜ Stanford CS224U Natural Language Under

08:27

P50 Adversarial Testing ｜ Stanford CS224U Natural Language Understanding ｜ Sprin

21:12

P51 Adversarial Training (and Testing) ｜ Stanford CS224U Natural Language Unders

11:16

P52 Probing ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2021

11:29

P53 Feature Attribution ｜ Stanford CS224U Natural Language Understanding ｜ Sprin

15:32

P54 Overview of Methods and Metrics ｜ Stanford CS224U Natural Language Understan

04:37

P55 Classifier Metrics ｜ Stanford CS224U Natural Language Understanding ｜ Spring

16:38

P56 Natural Language Generation Metrics ｜ Stanford CS224U Natural Language Under

11:35

P57 Data Organization ｜ Stanford CS224U Natural Language Understanding ｜ Spring

09:42

P58 Model Evaluation ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2

20:09

P59 Presenting Your Work： Final Papers ｜ Stanford CS224U Natural Language Unders

09:17

P60 Writing NLP papers ｜ Stanford CS224U Natural Language Understanding ｜ Spring

09:47

P61 NLP Conference Submissions ｜ Stanford CS224U Natural Language Understanding

21:06

P62 Giving Talks ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2021

10:23

P63 Conclusion ｜ Stanford CS224U Natural Language Understanding ｜ Spring 2021

01:27

斯坦福大学【中英⚡深度多任务与元学习 | 2020 CS330 Deep Multi-task and Meta Learning】

加州伯克利分校【中英⚡深度学习：深度神经网络设计、可视化与理解|2021 CS182 Deep Learning:】

麻省理工学院【中英⚡矩阵微积分及其在机器学习中的应用|2023 18.S096 Matrix Calculus For Machine Learning】

卡耐基梅隆大学【中英⚡机器学习与信号处理|2024 11-755 Machine Learning for Signal Processing】

卡内基梅隆大学【中英⚡LLM推理算法|11-763 Fall2025 CMU Inference Algorithms for Language Mode】

卡耐基梅隆大学【中英⚡深度学习导论习题课|11785 2023 Recitations】

加州伯克利分校【中英⚡人工智能|CS188 Spring2024, Artificial Intelligence】

加州大学伯克利分校【中英⚡机器学习导论|CS189 Fall 2025, Introduction to Machine Learning】

加州伯克利分校【中英⚡CS182 Fall 2025, 深度学习：深度神经网络设计、可视化与理解|Deep Learning】

加利福尼亚大学【中英⚡LLM与强化学习|Spring 2025, Reinforcement Learning of Large Language Model】

卡内基梅隆大学【中英⚡深度强化学习与控制|10-703 Fall 2018, Deep Reinforcement Learning and Control】

多伦多大学【中英⚡降维与流形学习|2023,Dimensionality Reduction and Manifold Learning】

IPAM【中英⚡深度学习与组合优化|2021 Deep Learning and Combinatorial Optimization】

伊利诺伊大学【中英⚡统计强化学习|CS542 Fall 2025, Statistical Reinforcement Learning】

麻省理工学院【中英⚡深度学习|6.7960 Fall 2024, Deep Learning】

麻省理工学院【中英⚡扩散模型导论|6.S183 IAP 2026, A Practical Introduction to Diffusion Models】

麻省理工学院【中英⚡流匹配与扩散模型导论| 6.S184 IAP 2025, An Introduction to Flow Matching and Dif】

加州大学圣地亚哥分校【中英⚡推荐系统与网络数据挖掘|CSE158 Fall 2019, Web Mining and Recommender Systems】

芝加哥大学【中英⚡机器学习数学基础|STAT27700 Fall 2019, Mathematical Foundations of Machine Lear】

宾夕法尼亚大学【中英⚡Python人工智能与机器学习基础|AI and Machine Learning Essentials with Python】

苏黎世联邦理工学院【中英⚡统计学习理论|Spring 2021, Statistical Learning Theory】

卡耐基梅隆大学【中英⚡机器学习数学基础|10-606 Summer 2017, Math Foundation for Machine Learning】

西蒙研究所【中英⚡异构数据源学习|Feb 2026, Learning from Heterogeneous Sources】

西蒙研究所【中英⚡大语言模型与认知科学、语言学及神经科学|LLMs, Cognitive Science, Linguistics, and Neurosci】

卡耐基梅隆大学【中英⚡自然语言处理|2020 Spring, Natural Language Processing】

卡内基梅隆大学【中英⚡高级自然语言处理|CMU Advanced NLP Spring 2026, Advanced NLP】

卡内基梅隆大学【中英⚡高级自然语言处理|CS 11-711 Fall 2024, Advanced NLP】

麻省理工学院【中英⚡流匹配与扩散模型|6.S184 2026, Flow Matching and Diffusion Models】

麻省理工学院【中英⚡计算音乐理论与分析|21M.383 Spring 2023, Computational Music Theory and Analysis

麻省理工学院【中英⚡实践深度学习|15.773 Spring 2024, 15.773 Hands-On Deep Learning】

米兰理工大学【中英⚡人工智能概述|Artificial Intelligence: an Overview】

米兰理工大学【中英⚡智能教学与学习：AI赋能|Smart Teaching and Learning with AI】

Matt Pocock【中英⚡面向真实工程师的AI编程|AI Coding for Real Engineers】

TJ Pitre & Ian Frost & Brad Frost【中英⚡AI与设计系统第一部分|AI & Design Systems part1】

TJ Pitre【中英⚡AI与设计系统第二部分|AI & Design Systems part2】

Dan Harrison【中英⚡Claude训练|Claude for Coaches】

Data Science Academy【中英⚡Claude AI系统架构师|Claude AI Systems Architect:】

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