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Title: GitHub - AMNETIZEN/machine-learning-notes: My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(2000+页)和视频链接 · GitHub

Open Graph Title: GitHub - AMNETIZEN/machine-learning-notes: My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(2000+页)和视频链接

X Title: GitHub - AMNETIZEN/machine-learning-notes: My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(2000+页)和视频链接

Description: My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(2000+页)和视频链接 - AMNETIZEN/machine-learning-notes

Open Graph Description: My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(2000+页)和视频链接 - AMNETIZEN/machine-learning-notes

X Description: My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(2000+页)和视频链接 - AMNETIZEN/machine-learning-notes

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https://github.com/AMNETIZEN/machine-learning-notes#live-machine-learning-class
https://github.com/AMNETIZEN/machine-learning-notes#中文机器学习研究线上课
微信二维码在这个链接https://github.com/roboticcam/machine-learning-notes/blob/master/files/class_qrcode.jpg
https://github.com/AMNETIZEN/machine-learning-notes#english-version
https://www.meetup.com/machine-learning-hong-kong/https://www.meetup.com/machine-learning-hong-kong/
https://github.com/AMNETIZEN/machine-learning-notes#learning-theory-classes
Class 1: Introductionhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/1.introduction.pdf
https://github.com/AMNETIZEN/machine-learning-notes#class-1-introduction
Class 2: Concentration Inequalityhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/2.concentration_inequality.pdf
https://github.com/AMNETIZEN/machine-learning-notes#class-2-concentration-inequality
Class 3: Rademarcher Complexityhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/3.rademarcher.pdf
https://github.com/AMNETIZEN/machine-learning-notes#class-3-rademarcher-complexity
Class 4: Neural Tangent Kernelhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/4.ntk.pdf
https://github.com/AMNETIZEN/machine-learning-notes#class-4-neural-tangent-kernel
Class 5: PAC Bayeshttps://github.com/roboticcam/machine-learning-notes/blob/master/files/5.pac_bayes.pdf
https://github.com/AMNETIZEN/machine-learning-notes#class-5-pac-bayes
Class 6: Johnson–Lindenstrauss lemmahttps://github.com/roboticcam/machine-learning-notes/blob/master/files/j_l_lemma.pdf
https://github.com/AMNETIZEN/machine-learning-notes#class-6-johnsonlindenstrauss-lemma
https://github.com/AMNETIZEN/machine-learning-notes#generative-ai
Transfomer with PyTorchhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/transformer.pdf
https://github.com/AMNETIZEN/machine-learning-notes#transfomer-with-pytorch
https://github.com/AMNETIZEN/machine-learning-notes#video-tutorial-to-these-notes-视频资料
Youtubehttps://www.youtube.com/channel/UConITmGn5PFr0hxTI2tWD4Q
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哔哩哔哩https://space.bilibili.com/327617676
优酷http://i.youku.com/i/UMzIzNDgxNTg5Ng
https://github.com/AMNETIZEN/machine-learning-notes#course-on-foundational-mathematics-in-machine-learning-机器学习基础数学课程
Class 1: Model Evaluationhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/foundation_model_evaluation.pdf
https://github.com/AMNETIZEN/machine-learning-notes#class-1-model-evaluation
Class 2: Decision Treehttps://github.com/roboticcam/machine-learning-notes/blob/master/files/foundation_decision_tree.pdf
https://github.com/AMNETIZEN/machine-learning-notes#class-2-decision-tree
Class 3: Simple Bayeshttps://github.com/roboticcam/machine-learning-notes/blob/master/files/foundation_simple_bayes.pdf
https://github.com/AMNETIZEN/machine-learning-notes#class-3-simple-bayes
Class 4: Regressionhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/foundation_regression.pdf
https://github.com/AMNETIZEN/machine-learning-notes#class-4-regression
Class 5: Neural Networkhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/foundation_neural_network.pdf
https://github.com/AMNETIZEN/machine-learning-notes#class-5-neural-network
Class 6: Unsupervised Learninghttps://github.com/roboticcam/machine-learning-notes/blob/master/files/foundation_unsupervised.pdf
https://github.com/AMNETIZEN/machine-learning-notes#class-6-unsupervised-learning
https://github.com/AMNETIZEN/machine-learning-notes#course-on-intemediate-mathematics-in-machine-learning-机器学习中级数学课程
Expectation Maximizationhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/intermediate_em.pdf
https://github.com/AMNETIZEN/machine-learning-notes#expectation-maximization
[gmm_demo.m]https://github.com/roboticcam/matlab_demos/blob/master/gmm_demo.m
[kmeans_demo.m]https://github.com/roboticcam/matlab_demos/blob/master/kmeans_demo.m
[bilibili video]https://www.bilibili.com/video/av23901379
[gmm_demo.m]https://github.com/roboticcam/matlab_demos/blob/master/gmm_demo.m
[kmeans_demo.m]https://github.com/roboticcam/matlab_demos/blob/master/kmeans_demo.m
[B站视频链接]https://www.bilibili.com/video/av23901379
Markov Chain Monte Carlohttps://github.com/roboticcam/machine-learning-notes/blob/master/files/intermediate_mcmc.pdf
https://github.com/AMNETIZEN/machine-learning-notes#markov-chain-monte-carlo
Variational Inferencehttps://github.com/roboticcam/machine-learning-notes/blob/master/files/intermediate_vb.pdf
https://github.com/AMNETIZEN/machine-learning-notes#variational-inference
[vb_normal_gamma.m]https://github.com/roboticcam/matlab_demos/blob/master/vb_normal_gamma.m
[bilibili video]https://www.bilibili.com/video/av24062247
[vb_normal_gamma.m]https://github.com/roboticcam/matlab_demos/blob/master/vb_normal_gamma.m
[B站视频链接]https://www.bilibili.com/video/av24062247
State Space Model (Dynamic model)https://github.com/roboticcam/machine-learning-notes/blob/master/files/intermediate_ssm.pdf
https://github.com/AMNETIZEN/machine-learning-notes#state-space-model-dynamic-model
[bilibili video]https://www.bilibili.com/video/av24225243
[kalman_demo.m]https://github.com/roboticcam/matlab_demos/blob/master/kalman_demo.m
[bilibili video]https://www.bilibili.com/video/av24132174
[B站视频链接]https://www.bilibili.com/video/av24225243
[kalman_demo.m]https://github.com/roboticcam/matlab_demos/blob/master/kalman_demo.m
[B站视频链接]https://www.bilibili.com/video/av24132174
https://github.com/AMNETIZEN/machine-learning-notes#sinovation-deecamp-创新工场deecamp讲义
DeeCamp 2019：Story of Softmaxhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/deecamp_2019.pdf
https://github.com/AMNETIZEN/machine-learning-notes#deecamp-2019story-of-softmax
DeeCamp 2018：When Probabilities meet Neural Networkshttps://github.com/roboticcam/machine-learning-notes/blob/master/files/DeeCamp2018_Xu_final.pptx
https://github.com/AMNETIZEN/machine-learning-notes#deecamp-2018when-probabilities-meet-neural-networks
https://github.com/AMNETIZEN/machine-learning-notes#deep-learning-research-topics-深度学习研究
Variance Reductionhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/variance_reduction.pdf
https://github.com/AMNETIZEN/machine-learning-notes#variance-reduction
New Research on Softmax functionhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/softmax.pdf
https://github.com/AMNETIZEN/machine-learning-notes#new-research-on-softmax-function
Mathematics for Generative Adversarial Networkshttps://github.com/roboticcam/machine-learning-notes/blob/master/files/GAN.pdf
https://github.com/AMNETIZEN/machine-learning-notes#mathematics-for-generative-adversarial-networks
A survey of traditional and state-of-the-art Generative Modelshttps://github.com/roboticcam/machine-learning-notes/blob/master/files/generative_models.pdf
https://github.com/AMNETIZEN/machine-learning-notes#a-survey-of-traditional-and-state-of-the-art-generative-models
Infinite Depth: NeuralODE and Adjoint Equationhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/neuralODE_Adjoint.pdf
https://github.com/AMNETIZEN/machine-learning-notes#infinite-depth-neuralode-and-adjoint-equation
Bayesian Inference and Deep Learning (Seminar Talk)https://github.com/roboticcam/machine-learning-notes/blob/master/files/bayesian_inference_deep_learning.pdf
https://github.com/AMNETIZEN/machine-learning-notes#bayesian-inference-and-deep-learning-seminar-talk
https://github.com/AMNETIZEN/machine-learning-notes#optimization-method-优化方法
Tutorial on Gradient Descend Researchhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/gradient_desend.pdf
https://github.com/AMNETIZEN/machine-learning-notes#tutorial-on-gradient-descend-research
Tutorial on Dualityhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/dual.pdf
https://github.com/AMNETIZEN/machine-learning-notes#tutorial-on-duality
Conjugate Gradient Descendhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/conjugate.pdf
https://github.com/AMNETIZEN/machine-learning-notes#conjugate-gradient-descend
https://github.com/AMNETIZEN/machine-learning-notes#deep-learning-basics-深度学习基础
Convolution Neural Networks: from basic to recent Researchhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/cnn_beyond.pdf
https://github.com/AMNETIZEN/machine-learning-notes#convolution-neural-networks-from-basic-to-recent-research
Restricted Boltzmann Machinehttps://github.com/roboticcam/machine-learning-notes/blob/master/files/rbm_cd.pdf
https://github.com/AMNETIZEN/machine-learning-notes#restricted-boltzmann-machine
https://github.com/AMNETIZEN/machine-learning-notes#3d-geometry-computer-vision-3d几何计算机视觉
3D Geometry Fundamentalshttps://github.com/roboticcam/machine-learning-notes/blob/master/files/cv_3d_foundation.pdf
https://github.com/AMNETIZEN/machine-learning-notes#3d-geometry-fundamentals
Recent Deep 3D Geometry based Researchhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/cv_3d_research.pdf
https://github.com/AMNETIZEN/machine-learning-notes#recent-deep-3d-geometry-based-research
https://github.com/AMNETIZEN/machine-learning-notes#reinforcement-learning-强化学习
Reinforcement Learning Basicshttps://github.com/roboticcam/machine-learning-notes/blob/master/files/dqn.pdf
https://github.com/AMNETIZEN/machine-learning-notes#reinforcement-learning-basics
Monto Carlo Tree Searchhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/mcts.pdf
https://github.com/AMNETIZEN/machine-learning-notes#monto-carlo-tree-search
Policy Gradienthttps://github.com/roboticcam/machine-learning-notes/blob/master/files/intermediate_policy_gradient.pdf
https://github.com/AMNETIZEN/machine-learning-notes#policy-gradient
https://github.com/AMNETIZEN/machine-learning-notes#natural-language-processing-自然语言处理
Word Embeddingshttps://github.com/roboticcam/machine-learning-notes/blob/master/files/word_vector.pdf
https://github.com/AMNETIZEN/machine-learning-notes#word-embeddings
Deep Natural Language Processinghttps://github.com/roboticcam/machine-learning-notes/blob/master/files/intermediate_nlp.pdf
https://github.com/AMNETIZEN/machine-learning-notes#deep-natural-language-processing
https://github.com/AMNETIZEN/machine-learning-notes#data-science-powerpoint-and-source-code-数据科学-powerpoint-和源代码
introduction to Deep Learning and ChatGPThttps://github.com/roboticcam/machine-learning-notes/blob/master/files/deep_learning_chatgpt.pdf
https://github.com/AMNETIZEN/machine-learning-notes#introduction-to-deep-learning-and-chatgpt
video linkhttps://www.bilibili.com/video/BV14M4y1R7h4/
30 minutes introduction to AI and Machine Learninghttps://github.com/roboticcam/machine-learning-notes/blob/master/files/30_min_AI.pptx
https://github.com/AMNETIZEN/machine-learning-notes#30-minutes-introduction-to-ai-and-machine-learning
[costFunction.m]https://github.com/roboticcam/matlab_demos/blob/master/costFunction.m
[soft_max.m]https://github.com/roboticcam/matlab_demos/blob/master/soft_max.m
[industry data science Jupyter notebook]https://github.com/roboticcam/machine-learning-notes/blob/master/files/industry_master_class.ipynb
Recommendation systemhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/recommendation.pdf
https://github.com/AMNETIZEN/machine-learning-notes#recommendation-system
https://github.com/AMNETIZEN/machine-learning-notes#probabilistic-model-概率模型课件
Probabilistic Estimationhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/probability.pdf
https://github.com/AMNETIZEN/machine-learning-notes#probabilistic-estimation
https://github.com/AMNETIZEN/machine-learning-notes#monte-carlo-inference-蒙特卡洛推理
Introduction to Monte Carlohttps://github.com/roboticcam/machine-learning-notes/blob/master/files/introduction_monte_carlo.pdf
https://github.com/AMNETIZEN/machine-learning-notes#introduction-to-monte-carlo
[adaptive_rejection_sampling.m]https://github.com/roboticcam/matlab_demos/blob/master/adaptive_rejection_sampling.m
[hybrid_gmm.m]https://github.com/roboticcam/matlab_demos/blob/master/hybrid_gmm.m
[adaptive_rejection_sampling.m]https://github.com/roboticcam/matlab_demos/blob/master/adaptive_rejection_sampling.m
[hybrid_gmm.m]https://github.com/roboticcam/matlab_demos/blob/master/hybrid_gmm.m
Markov Chain Monte Carlohttps://github.com/roboticcam/machine-learning-notes/blob/master/files/markov_chain_monte_carlo.pdf
https://github.com/AMNETIZEN/machine-learning-notes#markov-chain-monte-carlo-1
[lda_gibbs_example.m]https://github.com/roboticcam/matlab_demos/blob/master/lda_gibbs_example.m
[test_autocorrelation.m]https://github.com/roboticcam/matlab_demos/blob/master/test_autocorrelation.m
[gibbs.m]https://github.com/roboticcam/matlab_demos/blob/master/gibbs.m
[bilibili video]https://www.bilibili.com/video/av23980130
[lda_gibbs_example.m]https://github.com/roboticcam/matlab_demos/blob/master/lda_gibbs_example.m
[test_autocorrelation.m]https://github.com/roboticcam/matlab_demos/blob/master/test_autocorrelation.m
[gibbs.m]https://github.com/roboticcam/matlab_demos/blob/master/gibbs.m
[B站视频链接]https://www.bilibili.com/video/av23980130
Particle Filter (Sequential Monte-Carlo)https://github.com/roboticcam/machine-learning-notes/blob/master/files/particle_filter.pdf
https://github.com/AMNETIZEN/machine-learning-notes#particle-filter-sequential-monte-carlo
[bilibili video]https://www.bilibili.com/video/av24285449
[B站视频链接]https://www.bilibili.com/video/av24285449
https://github.com/AMNETIZEN/machine-learning-notes#advanced-probabilistic-model-高级概率模型课件
Bayesian Non Parametrics (BNP) and its inference basicshttps://github.com/roboticcam/machine-learning-notes/blob/master/files/non_parametrics.pdf
https://github.com/AMNETIZEN/machine-learning-notes#bayesian-non-parametrics-bnp-and-its-inference-basics
[dirichlet_process.m]https://github.com/roboticcam/matlab_demos/blob/master/dirichlet_process.m
[bilibili video]https://www.bilibili.com/video/av23881062
[Jupyter Notebook]https://github.com/roboticcam/python_machine_learning/blob/master/chinese_restaurant_process.ipynb
[dirichlet_process.m]https://github.com/roboticcam/matlab_demos/blob/master/dirichlet_process.m
[B站视频链接]https://www.bilibili.com/video/av23881062
[Jupyter Notebook]https://github.com/roboticcam/python_machine_learning/blob/master/chinese_restaurant_process.ipynb
Bayesian Non Parametrics (BNP) extensionshttps://github.com/roboticcam/machine-learning-notes/blob/master/files/non_parametrics_extensions.pdf
https://github.com/AMNETIZEN/machine-learning-notes#bayesian-non-parametrics-bnp-extensions
Completely Random Measure (early draft - written in 2015)https://github.com/roboticcam/machine-learning-notes/blob/master/files/random_measure.pdf
https://github.com/AMNETIZEN/machine-learning-notes#completely-random-measure-early-draft---written-in-2015
Sample correlated integers from HDP and Copulahttps://github.com/roboticcam/machine-learning-notes/blob/master/files/copula_dp.pdf
https://github.com/AMNETIZEN/machine-learning-notes#sample-correlated-integers-from-hdp-and-copula
IJCAI 2016 papershttps://www.ijcai.org/Proceedings/16/Papers/210.pdf
IJCAI2016论文https://www.ijcai.org/Proceedings/16/Papers/210.pdf
Determinantal Point Processhttps://github.com/roboticcam/machine-learning-notes/blob/master/files/dpp.pdf
https://github.com/AMNETIZEN/machine-learning-notes#determinantal-point-process
Determinantal Point Process Basics (updated)https://github.com/roboticcam/machine-learning-notes/blob/master/files/dpp_new.pdf
https://github.com/AMNETIZEN/machine-learning-notes#determinantal-point-process-basics-updated
https://github.com/AMNETIZEN/machine-learning-notes#special-thanks
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