https://github.com/zcemycl/mlreading-hub

List of casual implementations of machine learning models from scratch.
https://github.com/zcemycl/mlreading-hub

computer-vision machine-learning natural-language-processing probabilistic-machine-learning recommender-system reinforcement-learning

Last synced: 9 months ago
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List of casual implementations of machine learning models from scratch.

• Host: GitHub
• URL: https://github.com/zcemycl/mlreading-hub
• Owner: zcemycl
• Created: 2022-01-01T11:02:13.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2024-11-12T09:53:52.000Z (over 1 year ago)
• Last Synced: 2025-04-21T15:14:34.151Z (about 1 year ago)
• Topics: computer-vision, machine-learning, natural-language-processing, probabilistic-machine-learning, recommender-system, reinforcement-learning
• Language: Python
• Homepage:
• Size: 12.9 MB
• Stars: 2
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Collections of Machine Learning Readings [![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Fzcemycl%2Fmlreading-hub&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=hits&edge_flat=false)](https://hits.seeyoufarm.com)

### Machine Learning

* [Perceptron](notes/ml/Perceptron.ipynb)

* [ID3/C4.5 Decision Tree](notes/ml/Decision-Tree.ipynb)

* [Regression Tree](notes/ml/Regression-Tree.ipynb)

* Random Forest

* Gradient Boosting Machines

* [Ridge Regression](notes/ml/Ridge-Regression.ipynb)

* [Lasso Regression](notes/ml/Lasso-Regression.ipynb)

* Multiple Linear Regression

* Principal Component Regression

* [Time Series Linear Regression](notes/ml/Time-Series-Linear-Regression.ipynb)

* Autoregressive Moving Average Process

* [Support Vector Machine + Lagrange Multipliers](notes/ml/Support-Vector-Machine.ipynb)

### Probabilistic Machine Learning

* [Weight Distribution Contours](notes/pml/Distribution-Contour.ipynb)

* [Principal Component Analysis](https://github.com/zcemycl/ProbabilisticPerspectiveMachineLearning/blob/master/Machine%20Learning%20A%20Probabilistic%20Perspective/12LatentLinearModels/F12.5/12.5pcaImageDemo.ipynb)

* [Independent Component Analysis](https://github.com/zcemycl/ProbabilisticPerspectiveMachineLearning/blob/master/Machine%20Learning%20A%20Probabilistic%20Perspective/12LatentLinearModels/F12.20/12.20icaDemo.ipynb)

* [Naive Bayes](https://github.com/zcemycl/ProbabilisticPerspectiveMachineLearning/blob/master/Machine%20Learning%20A%20Probabilistic%20Perspective/3GMDD/F3.8/3.8naiveBayesBowDemo.ipynb)

* [Acceptance Rejection Sampling](notes/Acceptance-Rejection.ipynb)

* [Inverse Transform](notes/Inverse-Transform-Sampling.ipynb)

* [Importance Sampling](notes/ImportanceSampling.ipynb)

* [Dependent Sampling/Markovian Dice](notes/DependentSampling.ipynb)

* [Random Walk Metropolis](notes/Random-Walk-Metropolis.ipynb)

* Metropolis-Hastings

* Gibbs Sampling

* Hamitonian Monte Carlo (HMC)

* Kalman Filter

* Particle Filter (SMC)

* [K Means](notes/K-Means.ipynb)

* [Gaussian Mixture Models/EM algorithm](notes/EM-GMM2.ipynb)

* Bayesian GMMs

* [Hidden Markov Models/Known Latents training](notes/pml/HMM_visible.ipynb)

* [HMMs/Baum-Welch algorithm](notes/pml/EM_HMM_Sequence.ipynb)

* GMM-HMMs

* [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)

* Few Shot Learning/Siamese Network

* Gaussian Process

### Recommendation System

* [Smoothing/Wilson Lower Bound](notes/rs/rs_Rate-Popularity.ipynb)

* [Conjugate Priors/Online Learning](notes/rs/rs_Conjugate-Prior.ipynb)

* [D-Separation](notes/rs/rs-d-sep.ipynb)

* [Bayesian A/B Testings](notes/rs/rs_ABtestings.ipynb)

* [Causal Inference](notes/rs/rs_Causal-Inference.ipynb)

* [Page Rank](notes/rs/rs_Page-Rank.ipynb)

* [User-User Collaborative Filtering](notes/rs/rs_User-User-Collaborative-Filtering.ipynb)

* Item-Item Collaborative Filtering

* [Matrix Factorization/Alternating Least Squares](notes/rs/rs_Matrix-Factorization.ipynb)

* Bayesian Matrix Factorization/Gibbs Sampling

* [Embeddings](notes/rs/rs_Embedding.ipynb)/[Embeddings-DNN](notes/rs/rs_Deep-Neural-Network.ipynb)/[Inception-Residual-Network](notes/rs/rs-Residual-Learning.ipynb)

* Denoising Variational Autoencoders

* Restricted Boltzmann Machines

* Two Tower Model

* Wide & Deep Learning

### NLP

* [Term Frequency Inverse Document Frequency](notes/nlp/nlp_TFIDF.ipynb)

* [Embeddings/Word Analogy](notes/nlp/nlp_Embeddings.ipynb)

* [Bag-of-words/Text Classification](notes/nlp/nlp_Bag-of-words.ipynb)

* [Bigrams Language Models](notes/nlp/nlp_Bigrams-Language-Models.ipynb)

* [Logistic Regression/Neural Bigram/Gradient Descent](notes/nlp/nlp_Neural-Bigram.ipynb)

* [Bigrams with Autoencoder](notes/nlp/nlp_Bigram-Autoencoder.ipynb)

* CBOW/Skip-Gram/Negative Sampling

* [Glove/Matrix Factorization](notes/nlp/nlp_Glove-Matrix-Factorization.ipynb)

* [HMMs-Part of Speech Tagging](notes/nlp/nlp_HMMs-POS.ipynb)

* [Bidirectional-LSTM-Named Entity Recognition/F1-Score](notes/nlp/nlp_Named-Entity-Recognition-RNN.ipynb)

* [Parse Tree](notes/nlp/nlp_Recurrent-Tree-Neural-Network.ipynb)

* [TreeNN/Recursive(not Recurrent)NN/Sentiment Analysis/Binary Tree](notes/nlp/nlp_Recurrent-Tree-Neural-Network.ipynb)

* [Seq2seq Hierarchical Labels Classification](https://github.com/zcemycl/seq2seq-labelladder)

* Transformer

* GPT

### Computer Vision

* [Symbol Count with Edge Detection](notes/cv/convolution_connected_edge.ipynb)

* [Calibration](https://github.com/zcemycl/zcemycl.github.io/blob/master/resources/calibration.ipynb)

* [2D Homography](https://github.com/zcemycl/Robotics/blob/master/Perception/Logo%20Projection/LogoProjection.ipynb)

* [Arm Camera Calibration](https://github.com/zcemycl/Robotics/blob/master/DobotMagic/calibration/utils.py)

* Camera Model

* Single Shot Multibox detector

* You Only Look Once (YOLO)

* Segmantic Segmantation/Fully Convolution Network

* Segmantic Segmantation/Unet

* Human Pose Estimation/Stacked Hourglass Network

* [Neural Radiance Fields (NeRF)](notes/cv/nerf/)

* [Variational Autoencoder](https://github.com/zcemycl/self-work/blob/master/VAE/VAE2.ipynb)

* [Generative Adversarial Network](https://github.com/zcemycl/Matlab-GAN/blob/master/GAN/GAN.m)

* [InfoGAN](https://github.com/zcemycl/Matlab-GAN/blob/master/InfoGAN/InfoGAN.m)

* [Pix2pix](https://github.com/zcemycl/Matlab-GAN/blob/master/Pix2Pix/PIX2PIX.m)

* [CycleGAN](https://github.com/zcemycl/Matlab-GAN/blob/master/CycleGAN/CycleGAN.m)

* [Multi-task Network with Room-boundary-Guided Attention](https://github.com/zcemycl/TF2DeepFloorplan)

* Diffusion Model

* CLIP

### Reinforcement Learning

* [Multi-Arm Bandits](https://github.com/zcemycl/zcemycl.github.io/blob/master/resources/rlread.ipynb)

* [Genetic Algorithm](notes/rl/traveling_salesman.py)

* [Policy Iteration](https://github.com/zcemycl/Robotics/blob/master/Reinforcemnet%20Learning/PolicyIteration.ipynb)

* [Value Iteration](https://github.com/zcemycl/Robotics/blob/master/Reinforcemnet%20Learning/ValueIteration.ipynb)

* [Monte Carlo Methods/Blackjack](https://github.com/zcemycl/zcemycl.github.io/blob/master/resources/blackjack.ipynb)

* [SARSA](https://github.com/zcemycl/Robotics/blob/master/Reinforcemnet%20Learning/sarsa.ipynb)

* [Q-Learning](https://github.com/zcemycl/Robotics/blob/master/Reinforcemnet%20Learning/q-learning.ipynb)

* N step boostrapping

* Thompson Sampling

* Contextual Bandit

* Deep Q Learning

* Deep Convolutional Q Learning

* Twin Delayed DDPG

* Policy Gradient

* Generalized Advantage Estimation

* Trust Region Policy Optimization

* Monte Carlo Tree Search

### Model Intrepretability

* [Local Interpretable Model-agnostic Explanations](https://github.com/zcemycl/ProbabilisticPerspectiveMachineLearning/blob/master/LIME/LIME.ipynb)

### Model Compression

* Pruning

* Quantization

* Low-rank approximation

* Knowledge Distillation

* Neural Architecture Search

### Optimization

* [Gradient Descent](notes/ml/Ridge-Regression.ipynb)

* Sub-gradient Descent

* [Coordinate Descent](notes/ml/Lasso-Regression.ipynb)

### AI Fairness

### Speech

### Graph Neural Network

### Follow-ups