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【Algorithms Lab】计算几何 | Computational Geometry

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2025-09-28 18:50:00

https://www.youtube.com/playlist?list=PLMDFPuH4ZxUGe-qreA6cde5g7PywDvrsD

Mindofuture 发消息

祝大家学有所成

弹幕列表

【露露】淑女的品格【原创编舞】

【斯坦福大学 • 中英】人工智能：原理与技术（CS221， 2021）

【卡内基梅隆】高级自然语言处理 CMU CS 11-711（spring，2025）

【英伟达GTC人工智能大会 • 中英】NVIDIA GTC 2024

【科罗拉多大学 • 中英】计算机视觉 CSCI 5722

物理信息的机器学习（Physics Informed Machine Learning - PIML）

P01 Physics Informed Machine Learning - High Level Overview of AI and ML in Scie

47:27

P02 AI ML+Physics Part 1 - Choosing what to model [Physics Informed Machine Lear

43:27

P03 AI ML+Physics Part 2 - Curating Training Data [Physics Informed Machine Lear

36:11

P04 AI ML+Physics Part 3 - Designing an Architecture [Physics Informed Machine L

36:30

P05 AI ML+Physics Part 4 - Crafting a Loss Function [Physics Informed Machine Le

23:12

P06 AI ML+Physics Part 5 - Employing an Optimization Algorithm [Physics Informed

32:35

P07 AI ML+Physics - Recap and Summary [Physics Informed Machine Learning]

24:01

P08 AI ML+Physics - Preview of Upcoming Modules and Bootcamps [Physics Informed

19:01

P09 Deep Learning to Discover Coordinates for Dynamics - Autoencoders & Physics

26:56

P10 Sparse Identification of Nonlinear Dynamics (SINDy) - Sparse Machine Learnin

24:06

P11 Sparse Nonlinear Dynamics Models with SINDy, Part 2 - Training Data & Disamb

17:32

P12 Sparse Nonlinear Dynamics Models with SINDy, Part 3 - Effective Coordinates

19:40

P13 Sparse Nonlinear Dynamics Models with SINDy, Part 4 - The Library of Candida

27:17

P14 Sparse Nonlinear Dynamics Models with SINDy, Part 5 - The Optimization Algor

18:28

P15 Discrepancy Modeling with Physics Informed Machine Learning

19:10

P16 Hamiltonian Neural Networks (HNN) [Physics Informed Machine Learning]

19:24

P17 Lagrangian Neural Network (LNN) [Physics Informed Machine Learning]

19:49

P18 Neural Implicit Flow (NIF) [Physics Informed Machine Learning]

13:43

P19 Neural ODEs (NODEs) [Physics Informed Machine Learning]

24:38

P20 Python Symbolic Regression (PySR) [Physics Informed Machine Learning]

16:11

P21 Residual Networks (ResNet) [Physics Informed Machine Learning]

17:26

P22 Fourier Neural Operator (FNO) [Physics Informed Machine Learning]

17:46

P23 Deep Operator Networks (DeepONet) [Physics Informed Machine Learning]

17:17

P24 Physics Informed Neural Networks (PINNs) [Physics Informed Machine Learning]

34:32

【DeepLearning.AI】吴恩达人工智能应用专项公开课

【麻省理工 - 中英】从零构建完整的扩散模型 MIT 6.S184 | Flow Matching and Diffusion Model

【斯坦福大学 • 中英】自然语言理解 stanford XCS224U（Natural Language Understanding，Spring 2023）

【斯坦福大学 • 中英】基于深度学习的自然语言处理 CS224N（2021）

【卡内基梅隆 - 中英】计算机图形学 CMU 15-462 | 662（Computer Graphics）

【加州大学伯克利分校 - 中英】深度学习 STAT-157

【斯坦福大学 • 中英】图机器学习 CS224W（Machine Learning with Graphs，2021）

【纽约大学 - 中英】深度学习 NYU SP20（2020春）| Yann LeCun | 杨立琨

P01 Week 1 – Lecture - History, motivation, and evolution of Deep Learning

01:38:57

P02 Week 1 – Practicum - Classification, linear algebra, and visualisation

52:31

P03 Week 2 – Lecture - Stochastic gradient descent and backpropagation

01:43:17

P04 Week 2 – Practicum - Training a neural network

57:00

P05 Week 3 – Lecture - Convolutional neural networks

01:38:16

P06 Week 3 – Practicum - Natural signals properties and CNNs

48:22

P07 Week 4 – Practicum - Listening to convolutions

51:02

P08 Week 5 – Lecture - Optimisation

01:29:06

P09 Week 5 – Practicum - 1D multi-channel convolution and autograd

44:59

P10 Week 6 – Lecture - CNN applications, RNN, and attention

01:28:48

P11 Week 6 – Practicum - RNN and LSTM architectures

53:34

P12 Week 7 – Lecture - Energy based models and self-supervised learning

01:37:19

P13 Week 7 – Practicum - Under- and over-complete autoencoders

55:04

P14 Week 8 – Lecture - Contrastive methods and regularised latent variable model

01:39:26

P15 Week 8 – Practicum - Variational autoencoders

58:05

P16 Week 9 – Lecture - Group sparsity, world model, and generative adversarial n

01:58:25

P17 Week 9 – Practicum - (Energy-based) Generative adversarial networks

01:15:12

P18 Week 10 – Lecture - Self-supervised learning (SSL) in computer vision (CV)

02:00:42

P19 Week 10 – Practicum - The Truck Backer-Upper

01:00:26

P20 Week 11 – Lecture - PyTorch activation and loss functions

01:53:45

P21 Week 11 – Practicum - Prediction and Policy learning Under Uncertainty (PPUU

01:23:19

P22 Week 12 – Lecture - Deep Learning for Natural Language Processing (NLP)

01:40:57

P23 Week 12 – Practicum - Attention and the Transformer

01:18:02

P24 Week 13 – Lecture - Graph Convolutional Networks (GCNs)

02:00:23

P25 Week 13 – Practicum - Graph Convolutional Neural Networks (GCN)

01:10:02

P26 Week 14 – Lecture - Structured prediction with energy based models

02:07:31

P27 Week 14 – Practicum - Overfitting and regularization, and Bayesian neural ne

01:11:28

P28 Week 15 – Practicum part A - Inference for latent variable energy based mode

59:05

P29 Week 15 – Practicum part B - Training latent variable energy based models (E

58:57

P30 Matrix multiplication, signals, and convolutions

47:02

P31 Supervised and self-supervised transfer learning (with PyTorch Lightning)

01:11:24

【吴恩达】面向实际应用的机器学习工程专项课（MLOps）

【MLOps】《设计机器学习系统》书籍读书会（2024）

【图宾根大学 • 中英】计算机视觉 ML-4360 (Computer Vision)

【麻省理工】MIT6.801 机器视觉（Machine Vision, Fall 2020）

P01 Introduction to Machine Vision

01:19:53

P02 Image Formation, Perspective Projection, Time Derivative, Motion Field

01:21:14

P03 Time to Contact, Focus of Expansion, Direct Motion Vision Methods, Noise Gai

01:27:06

P04 Fixed Optical Flow, Optical Mouse, Constant Brightness Assumption

01:22:57

P05 TCC and FOR MontiVision Demos, Vanishing Point, Use of VPs in Camera Calibra

01:20:46

P06 Photometric Stereo, Noise Gain, Error Amplification, Eigenvalues and Eigenve

01:27:25

P07 Gradient Space, Reflectance Map, Image Irradiance Equation, Gnomonic Project

01:26:35

P08 Shading, Special Cases, Lunar Surface, Scanning Electron Microscope, Green's

01:26:10

P09 Shape from Shading, General Case - From First Order Nonlinear PDE to Five OD

01:26:02

P10 Characteristic Strip Expansion, Shape from Shading, Iterative Solutions

01:26:34

P11 Edge Detection, Subpixel Position, CORDIC, Line Detection (US 6,408,109)

01:26:55

P12 Blob Analysis, Binary Image Processing, Green's Theorem, Derivative and Inte

01:27:16

P13 Object Detection, Recognition and Pose Determination, PatQuick (US 7,016,539

01:23:15

P14 Inspection in PatQuick, Hough Transform, Homography, Position Determination,

01:26:11

P15 Alignment, PatMax, Distance Field, Filtering and Sub-Sampling (US 7,065,262)

01:24:08

P16 Fast Convolution, Low Pass Filter Approximations, Integral Images (US 6,457,

01:27:40

P17 Photogrammetry, Orientation, Axes of Inertia, Symmetry, Orientation

01:27:43

P18 Rotation and How to Represent It, Unit Quaternions, the Space of Rotations

01:26:31

P19 Absolute Orientation in Closed Form, Outliers and Robustness, RANSAC

01:25:48

P20 Space of Rotations, Regular Tessellations, Critical Surfaces, Binocular Ster

01:22:23

P21 Relative Orientation, Binocular Stereo, Structure, Quadrics, Calibration, Re

01:27:06

P22 Exterior Orientation, Recovering Position & Orientation, Bundle Adjustment,

01:32:30

P23 Gaussian Image, Solids of Revolution, Direction Histograms, Regular Polyhedr

01:29:56

【加州大学伯克利】深度强化学习 CS285（Deep Reinforcement Learning, fall 2023）

【普林斯顿】强化学习基础 ECE524（Foundations of Reinforcement Learning，2024）

【斯坦福计算机公开课】CS234强化学习

【图宾根大学】自动驾驶汽车课程 ML4340

【斯坦福】深度多任务学习与元学习 CS330（ Deep Multi-Task and Meta Learning，2022）

Google提示词教程

【斯坦福】CS236 深度生成模型（ Deep Generative Models，2023）

【麻省理工】高效机器学习 MIT 6.5940 EfficientML 2024 年秋季

【麻省理工 - 中英】计算机图形 MIT 6.837 | Introduction to Computer Graphics | 2021

【阿尔托大学 - 中英】联邦学习 CS-E4740（Federated Learning，spring 2025）

【卡耐基梅隆】CMU深度学习系统（算法与实现）

【斯坦福】CS230深度学习（吴恩达，Autumn 2018）

【斯坦福】CS25 Transformer 专题讲座（Transformers United，2023）

P01 Stanford CS25 V1 I Transformers United DL Models that have revolutionized

22:44

P02 Stanford CS25 V1 I Transformers in Language The development of GPT Models,

48:39

P03 Stanford CS25 V1 I Transformers in Vision Tackling problems in Computer Vi

01:08:37

P04 Stanford CS25 V1 I Decision Transformer Reinforcement Learning via Sequenc

01:20:43

P05 Stanford CS25 V1 I Mixture of Experts (MoE) paradigm and the Switch Transfo

01:05:44

P06 Stanford CS25 V1 I DeepMind's Perceiver and Perceiver IO new data family a

58:59

P07 Stanford CS25 V1 I Self Attention and Non-parametric transformers (NPTs)

01:05:43

P08 Stanford CS25 V1 I Transformer Circuits, Induction Heads, In-Context Learni

59:34

P09 Stanford CS25 V1 I Audio Research Transformers for Applications in Audio,

48:19

P10 Stanford CS25 V2 I Represent part-whole hierarchies in a neural network, Ge

52:48

P11 V2 I Introduction to Transformers w Andrej Karpathy

01:11:41

P12 V2 I Language and Human Alignment

01:06:21

P13 V2 I Emergent Abilities and Scaling in LLMs

01:07:49

P14 V2 I Strategic Games

53:21

P15 V2 I Robotics and Imitation Learning

01:16:08

P16 V2 I Common Sense Reasoning

01:15:06

P17 V2 I Biomedical Transformers

01:08:10

P18 V2 I Neuroscience-Inspired Artificial Intelligence

01:22:14

P19 V3 I Low-level Embodied Intelligence w Foundation Models

01:18:14

P20 V3 I Generalist Agents in Open-Ended Worlds

47:48

P21 V3 I How I Learned to Stop Worrying and Love the Transformer

01:20:38

P22 V3 I Recipe for Training Helpful Chatbots

01:08:49

P23 V3 I No Language Left Behind Scaling Human-Centered Machine Translation

52:28

P24 V3 I Beyond LLMs Agents, Emergent Abilities, Intermediate-Guided Reasoning

01:00:14

P25 V3 I Retrieval Augmented Language Models

01:19:27

P26 V4 I Overview of Transformers

01:17:29

P27 V4 I Jason Wei & Hyung Won Chung of OpenAI

01:17:07

P28 V4 I Aligning Open Language Models

01:16:21

P29 V4 I Demystifying Mixtral of Experts

01:04:32

P31 V4 I From Large Language Models to Large Multimodal Models

01:20:04

P32 V4 I Behind the Scenes of LLM Pre-training StarCoder Use Case

01:01:37

P33 V4 I Hyung Won Chung of OpenAI

36:31

【纽约大学 - 中英】深度学习 NYU SP21（2021春）| Yann LeCun | 杨立琨

P01 01 – History and resources

50:18

P02 01L – Gradient descent and the backpropagation algorithm

01:51:04

P03 02 – Neural nets - rotation and squashing

01:01:54

P04 02L – Modules and architectures

01:42:27

P05 03 – Tools, classification with neural nets, PyTorch implementation

01:05:48

P06 03L – Parameter sharing - recurrent and convolutional nets

01:59:48

P07 04L – ConvNet in practice

51:41

P08 04.1 – Natural signals properties and the convolution

01:09:13

P09 04.2 – Recurrent neural networks, vanilla and gated (LSTM)

01:05:37

P10 05L – Joint embedding method and latent variable energy based models (LV-EBM

01:51:32

P11 05.1 – Latent Variable Energy Based Models (LV-EBMs), inference

01:01:05

P12 05.2 – But what are these EBMs used for

10:43

P13 06L – Latent variable EBMs for structured prediction

01:48:54

P14 06 – Latent Variable Energy Based Models (LV-EBMs), training

01:04:49

P15 07L – PCA, AE, K-means, Gaussian mixture model, sparse coding, and intuitive

01:54:23

P16 07 – Unsupervised learning - autoencoding the targets

56:42

P17 08L – Self-supervised learning and variational inference

01:54:44

P18 08 – From LV-EBM to target prop to (vanilla, denoising, contractive, variati

01:00:35

P19 09L – Differentiable associative memories, attention, and transformers

02:00:29

P20 09 – AE, DAE, and VAE with PyTorch generative adversarial networks (GAN) and

01:07:51

P21 10L – Self-supervised learning in computer vision

01:36:14

P22 10 – Self cross, hard soft attention and the Transformer

01:12:01

P23 11L – Speech recognition and Graph Transformer Networks

01:55:04

P24 11 – Graph Convolutional Networks (GCNs)

57:34

P25 12L – Low resource machine translation

01:57:56

P26 12 – Planning and control

01:10:23

P27 13L – Optimisation for Deep Learning

01:51:33

P28 13 – The Truck Backer-Upper

01:01:22

P29 14L – Lagrangian backpropagation, final project winners, and Q&A session

02:12:36

P30 14 – Prediction and Planning Under Uncertainty

01:14:45

P31 AI2S Xmas Seminar - Dr. Alfredo Canziani (NYU) - Energy-Based Self-Supervise

03:45:28

P32 09P – Contrastive joint embedding methods (JEMs) for self-supervised learnin

56:52

P33 10P – Non-contrastive joint embedding methods (JEMs) for self-supervised lea

01:05:28

图神经网络 (Graph Neural Network)

【密歇根大学】计算机视觉的深度学习 | Deep Learning for Computer Vision | EECS 498-007 / 598-005

从零开始学NLP系列 | NLP Learn from Scratch

【韩国科学技术院】计算机图形学 KAIST CS580 Computer Graphics (Spring 2024)

【伯克利大学】Llm Agent | Uc Berkeley Rdi Cente | Cs 194/294 196 (Llm Agents)

【伯克利大学】高级大型语言模型智能体讲座 CS 194 294-280

彭罗斯: 为什么智能不是一个计算过程

31:12

【加州大学伯克利】机器学习硬件技术课程 | UC Berkeley | EE 290 | 2021

【gpu mode】Corsair，一个专为生成式人工智能推理设计的内存计算解决方案

01:38:09

【图宾根大学】深度学习的数学

【斯坦福大学】从零开始构建语言模型 CS336（Stanford CS336 Language Modeling from Scratch - 2025）

PIM Is All You Need: 使用CXL且无GPU的系统来进行LLM推理

52:23

强化学习之父理查德·萨顿，新加坡国立大学关于AI的科学趋势、社会影响和宇宙视角讲座

01:19:26

使用第一性原理方法理解深度学习 | Google DeepMind

01:17:33

【韩国科学技术院】扩散模型和应用 | KAIST CS492D, 2024 fall

在Excel中手动构建神经网络

43:54

【卡耐基梅隆】深度学习 CMU 11-785 | 2025

【科罗拉多大学】心灵与人工智能 | mind-machine-artificial-intelligence

机器学习3D数据处理

人工智能Fully Connected会议，旧金山 2025

P01_LLMOps_for_eval-driven_development_at_scale

20:02

P02_The_AI_that_solves_the_market-A_new_era_in_forecasting_with_natural_languag

22:04

P03_The_future_of_multi-agents_in_enterprises

19:55

P04_Physical_Intelligence_Unleashed-Building_robust_AI_at_scale_for_Edge_Device

22:14

P05_Scaling_GenAI_inference-Techniques__optimizations__and_real-world_lessons

15:52

P06_Run_the_model__not_the_risk-Powering_private_inference_for_enterprise_AI_an

15:12

P07_Synthetic_data_in_medical_device_AI-Challenges_and_opportunities

17:23

P08_One_size_doesn’t_fit_all-Building_AI_agents_specialized_for_your_enterprise

20:59

P09_A_T_cell_foundation_model_for_AI-powered_target_discovery_and_precision_med

15:36

P10_Beyond_RAG-Production-ready_AI_agents_powered_by_enterprise-scale_data

18:51

P11_Label_factory-LLMs_for_training_small_language_classification_models_at_sca

17:24

P12_From_playground_to_production-Turbocharging_GenAI_innovation_with_AWS_and_W

20:53

P13_Fueling_innovation_at_scale-Inside_Pinterests_machine_learning_platform

20:03

P14_Training_video_models_at_scale_with_Adobe_Firefly

21:33

P15_Efficient_Inference_with_Command_A-Optimizing_Speed_and_Cost_for_Enterprise

20:39

P16_Building_future-ready_AI_with_agents_&_data_flywheels-Insights_from_NVIDIA’

19:40

P17_The_open_source_AI_compute_tech_stack-Kubernetes_+_Ray_+_PyTorch_+_vLLM

18:35

P18_From_research_to_reality_in_the_age_of_(Gen)AI

23:45

P19_Shipping_smart_agents-lessons_from_the_frontlines

17:28

P20_PyTorch-The_open_language_of_AI

30:16

P21_Building_super_intelligent_tools

27:35

P22_Weights_&_Biases_and_CoreWeave-Fully_Connected_2025_Keynote

38:09

P23_AI’s_$600B_Question-Scaling_for_what_comes_next

28:36

【麻省理工】深度学习导论 MIT 6.S191 | Introduction To Deep Learning, 2025

【加州大学伯克利】计算机视觉：视觉数据的深度学习 | Cs 198 126: Deep Learning For Visual Data

【Pytorch】编译器技术 | Pytorch Compiler

Pytorch Day France 2025

深度学习中的Transformer架构 | Transformers In Deep Learning | Learn With Jay

【图宾根大学】概率机器学习 | Probabilistic Machine Learning 2025

Nvidia Scipy 2025 Sessions

强化学习专项【阿尔伯塔大学】强化学习基础 | Fundamentals Of Reinforcement Learning

强化学习专项【阿尔伯塔大学】基于样本的学习方法 | Sample Based Learning Methods

强化学习专项【阿尔伯塔大学】利用函数逼近进行预测和控制 | Prediction Control Function Approximation

强化学习专项【阿尔伯塔大学】完整的强化学习系统 | Complete Reinforcement Learning System

【麻省理工】基础模型与生成式人工智能 Mit 6.S087 | Foundation Models & Generative Ai

机器学习数学专项【帝国理工学院】机器学习数学：线性代数 | Linear Algebra Machine Learning

机器学习数学专项【帝国理工学院】机器学习数学：多元微积分 | Multivariate Calculus Machine Learning

机器学习数学专项【帝国理工学院】机器学习数学：主成分分析Pca | Pca Machine Learning

Full Stack 深度学习 | Full Stack Deep Learning 2022

【伯克利】深度学习 | Deep Learning: Cs 182 Spring 2021

【图宾根大学】统计机器学习 | Statistical Machine Learning 2020

Langchain初学者入门2025

Langgraph初学者入门2025

Deepmind + Ucl 深度强化学习 | Deepmind X Ucl | Deep Learning Lecture Series 2021

生成式Ai的数学基础 | Mathematical Foundations Of Generative Ai

【阿姆斯特丹大学】群等变深度学习 | Group Equivariant Deep Learning (Uva 2022)

【康奈尔大学】深度生成模型 | Deep Generative Models, Cornell Tech Cs 6785

机器学习人员的神经科学 | Neuroscience For Machine Learners

【斯坦福大学】计算机视觉的深度学习 | Stanford Cs231n Deep Learning For Computer Vision 2025

【伯克利大学】深度无监督学习 | Cs294 158 Deep Unsupervised Learning Uc Berkeley 2024

神经网络课程 | 人工智能之父杰弗里辛顿 | Geoffrey Hinton | Neural Networks 2012

Pytorch初学者教程 | Pytorch Beginner Series

【麻省理工】如何用Ai做任何事 | Mit How To Ai (Almost) Anything, Spring 2025

神经符号夏季学校 | 理查德萨顿 | Neuro Symbolic Ai Summer School 2025

【卡耐基梅隆】概率图形模型 | Probabilistic Graphical Models 2020

【卡塔尔大学】深度学习Cmps497 | Cmps497 Deep Learning | Fall 2024

理解深度学习 | Understanding Deep Learning

高级机器学习主题 | Advanced Machine Learning:

图机器学习会议 | Learning On Graphs Conference 2024

【Microsoft】Mcp协议入门 | Model Context Protocol (Mcp)

【Databricks】Llm模型底层基础 | Llm: Foundation Models From The Ground Up

机器学习的线性代数及Python实现 | Linear Algebra For Machine Learning

【Algorithms Lab】计算几何 | Computational Geometry

实用深度学习教程1 | Jeremy Howard

实用深度学习教程2 | Jeremy Howard

几何深度学习 | Ammi Geometric Deep Learning Course

降维技术 | Dimensionality Reduction

图表征学习讲座 | Graph Representation Learning

【加利福尼亚大学】图形模型中的学习 | Cs274b | Learning In Graphical Models (Spring 2021)

【康奈尔大学】智能系统的机器学习 | Cs4780/Cs5780 Machine Learning For Intelligent Systems 2018

【卡内基梅隆】多模态机器学习 | Cmu Multimodal Machine Learning, Fall 2023

【亚利桑那州立大学】知识表征和推理 | Asu Cse 579 Knowledge Representation 2018

【NPTEL】计算机视觉的深度学习

图神经网络 | Graph Neural Networks

【康奈尔大学】应用机器学习 | Cs 5787, Applied Machine Learning, Cornell 2020

自动驾驶汽车专项【多伦多大学】自动驾驶汽车简介 | Intro Self Driving Cars

自动驾驶汽车专项【多伦多大学】自动驾驶汽车的状态估计和定位 | State Estimation Localization Self Driving Cars

自动驾驶汽车专项【多伦多大学】自动驾驶汽车的视觉感知 | Visual Perception Self Driving Cars

自动驾驶汽车专项【多伦多大学】自动驾驶汽车的运动规划 | Motion Planning Self Driving Cars

深度强化学习基础 | Foundations Of Deep Rl

深度学习数学基础入门 | Introduction To Mathematics Of Deep Learning

【麻省理工】机器学习及其广泛应用的矩阵微积分| Mit 18.S096 | Matrix Calculus Iap 2023

图表征学习课程 | Graph Representation Learning Course

【斯坦福】机器学习人类偏好 | Stanford Cs329h Machine Learning Human Preferences

【Ipam】深度学习与建模的多模态成像 | Multi Modal Imaging With Deep Learning And Modeling 2022

2025 Pytorch大会 | Pytorch Conference 2025

【滑铁卢大学】优化技术课程 | Optimization Techniques W2023

【加州大学】大型语言模型的强化学习 | Reinforcement Learning Of Large Language Models 2025

【Sebastian Raschka】深度学习和生成式模型 Intro to Deep Learning and Generative Models 2021

直觉式深度学习 | Intuitive Deep Learning

【Flatiron研究所】自监督学习：人工智能的最后边界 2025 | Self Supervised Learning

【Andrej Karpathy】神经网络：从Zero到Hero | 安德烈卡帕西

【Basira Lab】深度图学习 | Deep Graph Learning

【卡内基梅隆】语言模型与推理 Cmu 2025| Introduction To Language Models And Inference

【凯尔大学】机器学习优化方法 | Optimization Methods For Machine Learning And Engineering 2021

【比科卡大学】机器学习讲座 | Machine Learning | Moe | Grpah | Rlhf | Llm | Ai Safty | 2024

什么使表征有用？ | What makes representations useful?

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【多伦多大学】降维与流形学习 | Introduction To Dimensionality Reduction And Manifold Learning

【西蒙斯研究所】Llms、认知科学、语言学和神经科学 | Cognitive Science | Linguistics | Neuroscience

量子深度学习 | Quantum Deep Learning

【斯坦福】深度强化学习 | Stanford Cs224r Deep Reinforcement Learning 2025

【华盛顿大学】神经科学入门 | Introduction To Neuroscience | Bing Wen Brunton

【哈佛大学】机器学习几何学 | Geometry Of Machine Learning

【麻省理工】数值算法应用于计算与机器学习 Mit 6.7350 | Numerical Algorithms For Machine Learning 2025

【韩国高等科学院】扩散模型与流模型 | Diffusion And Flow Models 2025 Kaist

【卡耐基梅隆】深度学习导论 Fall 2025 | Cmu Introduction To Deep Learning 11 785

【印度理工学】机器学习优化 | Cs 769 Optimization In Machine Learning

【Evidently Ai】面向构建者的Llm评估 | Llm Evaluation For Builders

【Ipam】深度学习与组合优化 | Deep Learning And Combinatorial Optimization 2021

【麻省理工】深度学习实践课 Mit 15.773 | Hands On Deep Learning Spring 2024

【斯坦福】Transformer和大语言模型 | Stanford Cme295 | Transformers & Llms

【德国萨尔大学】高维分析：随机矩阵和机器学习 | High Dimensional Analysis

【普渡大学】深度学习中的优化 | Optimization For Deep Learning | Purdue University

【印度理工学院】知识表示与推理 | Knowledge Representation And Reasoning

【Deeplearning】吴恩达：机器学习和数据科学线性代数1 | Machine Learning Linear Algebra

【Deeplearning】吴恩达：机器学习和数据科学微积分2 | Machine Learning Calculus

【Deeplearning】吴恩达：机器学习和数据科学概率与统计3 | Machine Learning Probability And Statistics

【斯坦福】概率图形模型1：表示法 | Probabilistic Graphical Models

【斯坦福】概率图形模型2：推理 | Probabilistic Graphical Models 2 Inference

【斯坦福】概率图形模型3：学习 | Probabilistic Graphical Models 3 Learning

【Deepmind】谷歌Agi安全课程 | Google Deepmind Agi Safety Course

图学习会议 | Learning On Graphs Conference 2025

柏拉图空间研讨会 | 人工智能 | Platonic Space Symposium 2025

【菲尔兹研究所】网络科学领域实验室 | Fields Lab For Network Science

【多伦多大学】强化学习 2025 | Uoft Reinforcement Learning Fall 2025

【慕尼黑工业大学】多视角几何 | Multi View Geometry

因果强化学习讲座 | Causal Rl

【Hhu Dusseldorf】表征学习 | Representation Learning Msc Course Ss2023

图Rag教程 | Graphrag

【纽约大学】婴儿意识 | Nyu Infant Consciousness

生成式人工智能、可信赖的人工智能与自监督学习 | Gen Ai Trustable Ai Self Supervised Learning 2023

【纽约大学】深度学习的哲学 | Philosophy Of Deep Learning 2024

【麻省理工】深度学习 | Mit 6.7960 Deep Learning Fall 2024

【哈佛大学】人工智能安全与对齐 | Harvard Cs 2881R Ai Safety And Alignment

【卡内基梅隆】人工智能系统软件工程 | 17 445/645 Software Engineering For Ai Enabled Systems

【德克萨斯大学】高级机器学习优化 | Advanced Topics In Optimization For Machine Learning

【斯坦福】人工智能原理与技术 | Cs221 Artificial Intelligence Principles And Techniques

【斯坦福】深度学习 | Stanford Cs230 Deep Learning I Autumn 2025

【麻省理工】识别、估计与学习 | Mit 2.160 Identification Estimation And Learning

【卡耐基梅隆】高级自然语言处理 | Cmu Advanced Nlp Fall 2025

【普林斯顿】理论机器学习 | Princeton Cos511 Theoretical Machine Learning 2026

【成均馆大学】表征理论 | Representation Theory

【麻省理工】流匹配和扩散模型 | Mit 6.S184 Flow Matching And Diffusion Model 2026

【菲尔兹研究所】数学人工智能研讨会 | Mathematical Ai Seminar

【威斯康星大学】Silo人工智能研讨会 | Silo Seminar Series

【阿尔伯塔大学】大模型推理的数学原理 | Math Of Reasoning In Llms 2025

【普林斯顿】机器学习优化 | Cos512 Optimization For Machine Learning

【Iiia Csic】机器学习优化 | Multi Agent Reinforcement Learning

【Hugging Face】深入探讨使用强化学习训练Agent

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【Nathan Lambert】强化学习与人类反馈 | Rlhf Book Course

【卡内基梅隆】深度学习 | Cmu Introduction To Deep Learning 11785 2026

【Anthropic】Code With Claude 2026 | San Francisco

【西蒙斯研究所】从异构来源学习 | Learning From Heterogeneous Sources

【西蒙斯研究所】深度强化学习 | Deep Reinforcement Learning

【卡耐基梅隆】高级自然语言处理 | Cmu Advanced Nlp Spring 2026

【卡耐基梅隆】现代人工智能讲座 | 10 202 Intro To Modern Ai Online Lectures

【西蒙斯研究所】优化与采样中的几何方法 | Geometric Methods In Optimization And Sampling

【普林斯顿大学】数值计算和机器学习的数学 | Cos 302 Mathematics For Numerical Computation And Ml

【海德堡大学】机器学习与物理学 | Machine Learning And Physics 2025

【多伦多大学】应用深度学习 | Uoft Applied Deep Learning Winter 2026

【多伦多大学】深度生成AI | Uoft Deep GenAI Course Summer2025

【麻省理工】扩散模型实践 | Mit 6.S183 A Practical Introduction To Diffusion

【斯坦福】从零开始的语言建模 | Stanford Cs336 Language Modeling From Scratch 2026

【亚利桑那州立大学】强化学习与最优控制 | Reinforcement Learning And Optimal Control

【哥伦比亚大学】因果推理 | Causal Inference

【伯克利】大语言模型智能体 | Llm Agents | Uc Berkeley Cs294 196 Fall 2025

【瑞士洛桑联邦理工】机器学习优化 | Epfl Cs439 Optimization For Machine Learning

【卡内基梅隆】生产中的机器学习 | Ml In Production Course Cmu 2026

【华盛顿州立大学】优化原理 | Principles Of Optimization 2019

【麻省理工】深度学习导论 | Mit 6.S191 Deep Learning 2026

【普林斯顿】深度学习数学 | Minicourse Mathematics Of Deep Learning

【斯坦福大学】Diffusion 视觉大模型 | Stanford Cme296 Diffusion Large Vision Models 2026

【圣地亚哥大学】大型语言模型推理 | Llm Inference

【宾夕法尼亚大学】带条件保证的学习 | Cis 6200 Learning With Conditional Guarantees

【洛桑联邦理工学院】人工生命 | Artificial Life

【特伦托大学】物理信息机器学习 | Physics Informed Machine Learning

【Vizuara】扩散模型原理精讲 | The Principles Of Diffusion Models

【Vizuara】Ai 论文精读方法与习惯 | Reading Ai Ml Research Papers

【多伦多大学】生成式人工智能 | Uoft Generative Ai Summer 2026

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