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Collections of Machine Learning Readings [![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Fzcemycl%2Fmlreading-hub\u0026count_bg=%2379C83D\u0026title_bg=%23555555\u0026icon=\u0026icon_color=%23E7E7E7\u0026title=hits\u0026edge_flat=false)](https://hits.seeyoufarm.com)\n\n### Machine Learning\n* [Perceptron](notes/ml/Perceptron.ipynb)\n* [ID3/C4.5 Decision Tree](notes/ml/Decision-Tree.ipynb)\n* [Regression Tree](notes/ml/Regression-Tree.ipynb)\n* Random Forest\n* Gradient Boosting Machines\n* [Ridge Regression](notes/ml/Ridge-Regression.ipynb)\n* [Lasso Regression](notes/ml/Lasso-Regression.ipynb)\n* Multiple Linear Regression\n* Principal Component Regression\n* [Time Series Linear Regression](notes/ml/Time-Series-Linear-Regression.ipynb)\n* Autoregressive Moving Average Process\n* [Support Vector Machine + Lagrange Multipliers](notes/ml/Support-Vector-Machine.ipynb)\n\n### Probabilistic Machine Learning\n* [Weight Distribution Contours](notes/pml/Distribution-Contour.ipynb)\n* [Principal Component Analysis](https://github.com/zcemycl/ProbabilisticPerspectiveMachineLearning/blob/master/Machine%20Learning%20A%20Probabilistic%20Perspective/12LatentLinearModels/F12.5/12.5pcaImageDemo.ipynb)\n* [Independent Component Analysis](https://github.com/zcemycl/ProbabilisticPerspectiveMachineLearning/blob/master/Machine%20Learning%20A%20Probabilistic%20Perspective/12LatentLinearModels/F12.20/12.20icaDemo.ipynb)\n* [Naive Bayes](https://github.com/zcemycl/ProbabilisticPerspectiveMachineLearning/blob/master/Machine%20Learning%20A%20Probabilistic%20Perspective/3GMDD/F3.8/3.8naiveBayesBowDemo.ipynb)\n* [Acceptance Rejection Sampling](notes/Acceptance-Rejection.ipynb)\n* [Inverse Transform](notes/Inverse-Transform-Sampling.ipynb)\n* [Importance Sampling](notes/ImportanceSampling.ipynb)\n* [Dependent Sampling/Markovian Dice](notes/DependentSampling.ipynb)\n* [Random Walk Metropolis](notes/Random-Walk-Metropolis.ipynb)\n* Metropolis-Hastings\n* Gibbs Sampling\n* Hamitonian Monte Carlo (HMC)\n* Kalman Filter\n* Particle Filter (SMC)\n* [K Means](notes/K-Means.ipynb)\n* [Gaussian Mixture Models/EM algorithm](notes/EM-GMM2.ipynb)\n* Bayesian GMMs\n* [Hidden Markov Models/Known Latents training](notes/pml/HMM_visible.ipynb)\n* [HMMs/Baum-Welch algorithm](notes/pml/EM_HMM_Sequence.ipynb)\n* GMM-HMMs\n* [Bayesian Polynomial Regression](https://github.com/zcemycl/ProbabilisticPerspectiveMachineLearning/blob/master/Probabilistic%20Machine%20Learning/Introduction%20to%20Probabilistic%20Machine%20Learning/Bayesian%20inference%20and%20prediction%20with%20finite%20regression%20models.ipynb)\n* Few Shot Learning/Siamese Network\n* Gaussian Process\n\n### Recommendation System\n* [Smoothing/Wilson Lower Bound](notes/rs/rs_Rate-Popularity.ipynb)\n* [Conjugate Priors/Online Learning](notes/rs/rs_Conjugate-Prior.ipynb)\n* [D-Separation](notes/rs/rs-d-sep.ipynb)\n* [Bayesian A/B Testings](notes/rs/rs_ABtestings.ipynb)\n* [Causal Inference](notes/rs/rs_Causal-Inference.ipynb)\n* [Page Rank](notes/rs/rs_Page-Rank.ipynb)\n* [User-User Collaborative Filtering](notes/rs/rs_User-User-Collaborative-Filtering.ipynb)\n* Item-Item Collaborative Filtering\n* [Matrix Factorization/Alternating Least Squares](notes/rs/rs_Matrix-Factorization.ipynb)\n* Bayesian Matrix Factorization/Gibbs Sampling\n* [Embeddings](notes/rs/rs_Embedding.ipynb)/[Embeddings-DNN](notes/rs/rs_Deep-Neural-Network.ipynb)/[Inception-Residual-Network](notes/rs/rs-Residual-Learning.ipynb)\n* Denoising Variational Autoencoders\n* Restricted Boltzmann Machines\n* Two Tower Model\n* Wide \u0026 Deep Learning\n\n### NLP\n* [Term Frequency Inverse Document Frequency](notes/nlp/nlp_TFIDF.ipynb)\n* [Embeddings/Word Analogy](notes/nlp/nlp_Embeddings.ipynb)\n* [Bag-of-words/Text Classification](notes/nlp/nlp_Bag-of-words.ipynb)\n* [Bigrams Language Models](notes/nlp/nlp_Bigrams-Language-Models.ipynb)\n* [Logistic Regression/Neural Bigram/Gradient Descent](notes/nlp/nlp_Neural-Bigram.ipynb)\n* [Bigrams with Autoencoder](notes/nlp/nlp_Bigram-Autoencoder.ipynb)\n* CBOW/Skip-Gram/Negative Sampling\n* [Glove/Matrix Factorization](notes/nlp/nlp_Glove-Matrix-Factorization.ipynb)\n* [HMMs-Part of Speech Tagging](notes/nlp/nlp_HMMs-POS.ipynb)\n* [Bidirectional-LSTM-Named Entity Recognition/F1-Score](notes/nlp/nlp_Named-Entity-Recognition-RNN.ipynb)\n* [Parse Tree](notes/nlp/nlp_Recurrent-Tree-Neural-Network.ipynb)\n* [TreeNN/Recursive(not Recurrent)NN/Sentiment Analysis/Binary Tree](notes/nlp/nlp_Recurrent-Tree-Neural-Network.ipynb)\n* [Seq2seq Hierarchical Labels Classification](https://github.com/zcemycl/seq2seq-labelladder)\n* Transformer\n* GPT\n\n### Computer Vision\n* [Symbol Count with Edge Detection](notes/cv/convolution_connected_edge.ipynb)\n* [Calibration](https://github.com/zcemycl/zcemycl.github.io/blob/master/resources/calibration.ipynb)\n* [2D Homography](https://github.com/zcemycl/Robotics/blob/master/Perception/Logo%20Projection/LogoProjection.ipynb)\n* [Arm Camera Calibration](https://github.com/zcemycl/Robotics/blob/master/DobotMagic/calibration/utils.py)\n* Camera Model\n* Single Shot Multibox detector\n* You Only Look Once (YOLO)\n* Segmantic Segmantation/Fully Convolution Network\n* Segmantic Segmantation/Unet\n* Human Pose Estimation/Stacked Hourglass Network\n* [Neural Radiance Fields (NeRF)](notes/cv/nerf/)\n* [Variational Autoencoder](https://github.com/zcemycl/self-work/blob/master/VAE/VAE2.ipynb)\n* [Generative Adversarial Network](https://github.com/zcemycl/Matlab-GAN/blob/master/GAN/GAN.m)\n* [InfoGAN](https://github.com/zcemycl/Matlab-GAN/blob/master/InfoGAN/InfoGAN.m)\n* [Pix2pix](https://github.com/zcemycl/Matlab-GAN/blob/master/Pix2Pix/PIX2PIX.m)\n* [CycleGAN](https://github.com/zcemycl/Matlab-GAN/blob/master/CycleGAN/CycleGAN.m)\n* [Multi-task Network with Room-boundary-Guided Attention](https://github.com/zcemycl/TF2DeepFloorplan)\n* Diffusion Model\n* CLIP\n\n### Reinforcement Learning\n* [Multi-Arm Bandits](https://github.com/zcemycl/zcemycl.github.io/blob/master/resources/rlread.ipynb)\n* [Genetic Algorithm](notes/rl/traveling_salesman.py)\n* [Policy Iteration](https://github.com/zcemycl/Robotics/blob/master/Reinforcemnet%20Learning/PolicyIteration.ipynb)\n* [Value Iteration](https://github.com/zcemycl/Robotics/blob/master/Reinforcemnet%20Learning/ValueIteration.ipynb)\n* [Monte Carlo Methods/Blackjack](https://github.com/zcemycl/zcemycl.github.io/blob/master/resources/blackjack.ipynb)\n* [SARSA](https://github.com/zcemycl/Robotics/blob/master/Reinforcemnet%20Learning/sarsa.ipynb)\n* [Q-Learning](https://github.com/zcemycl/Robotics/blob/master/Reinforcemnet%20Learning/q-learning.ipynb)\n* N step boostrapping\n* Thompson Sampling\n* Contextual Bandit\n* Deep Q Learning\n* Deep Convolutional Q Learning\n* Twin Delayed DDPG\n* Policy Gradient\n* Generalized Advantage Estimation\n* Trust Region Policy Optimization\n* Monte Carlo Tree Search\n\n### Model Intrepretability\n* [Local Interpretable Model-agnostic Explanations](https://github.com/zcemycl/ProbabilisticPerspectiveMachineLearning/blob/master/LIME/LIME.ipynb)\n\n### Model Compression\n* Pruning\n* Quantization\n* Low-rank approximation\n* Knowledge Distillation\n* Neural Architecture Search\n\n\n### Optimization\n* [Gradient Descent](notes/ml/Ridge-Regression.ipynb)\n* Sub-gradient Descent\n* [Coordinate Descent](notes/ml/Lasso-Regression.ipynb)\n\n### AI Fairness\n\n### Speech\n\n### Graph Neural Network\n\n### Follow-ups\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzcemycl%2Fmlreading-hub","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzcemycl%2Fmlreading-hub","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzcemycl%2Fmlreading-hub/lists"}