machine-learning
- machine-learning
- Documentation Listings
- deep learning
- model deployment
- operation research
- reinforcement learning
- ad
- search
- time series
- projects
- ab tests
- model selection
- dim reduct
- recsys
- trees
- clustering
- keras
- text classification
- regularization
- networkx
- association rule
- big data
- data science is software
- ga
- unbalanced
- clustering old
- linear regression
- Python Programming
- Documentation Listings
machine-learning
This is a continuously updated repository that documents personal journey on learning data science, machine learning related topics.
Goal: Introduce machine learning contents in Jupyter Notebook format. The content aims to strike a good balance between mathematical notations, educational implementation from scratch using Python’s scientific stack including numpy, numba, scipy, pandas, matplotlib, pyspark etc. and open-source library usage such as scikit-learn, fasttext, huggingface, onnx, xgboost, lightgbm, pytorch, keras, tensorflow, gensim, h2o, ortools, ray tune etc.
Documentation Listings
deep learning
Curated notes on deep learning.
- Softmax Regression from scratch. [nbviewer][html]
- Softmax Regression - Tensorflow hello world. [nbviewer][html]
- Multi-layers Neural Network - Tensorflow. [nbviewer][html]
- Convolutional Neural Network (CNN) - Tensorflow. [nbviewer][html]
- Recurrent Neural Network (RNN).
- Word2vec (skipgram + negative sampling) using Gensim. [nbviewer][html]
- Sequence to Sequence Neural Network (Seq2Seq).
- Subword Tokenization.
- Fasttext.
- Graph Neural Network (GNN).
- Transformer.
- Transformer, Attention is All you Need - PyTorch, Huggingface Datasets. [nbviewer][html]
- Machine Translation with Huggingface Transformers mT5. [nbviewer][html]
- Fine Tuning Pre-trained Encoder on Question Answer Task. [nbviewer][html]
- Training Bi-Encoder Models with Contrastive Learning Notes. [nbviewer][html]
- Sentence Transformer: Training Bi-Encoder via Contrastive Loss. [nbviewer][html]
- Introduction to CLIP (Contrastive Language-Image Pre-training), LiT, ViT [nbviewer][html]
- Self Supervised (SIMCLR) versus Supervised Contrastive Learning. [nbviewer][html]
- Multilingual Sentence Embedding with LLM and PEFT LoRA (PyTorch Lightning) [nbviewer][html]
- LLM Pairwise Judge (PyTorch Lightning) [nbviewer][html]
- Direct Preference Optimization (DPO) [nbviewer][html]
- Tabular
model deployment
- FastAPI & Azure Kubernetes Cluster. End to end example of training a model and hosting it as a service. [folder]
- Quick Intro to Gradient Boosted Tree Inferencing. [nbviewer][html]
- Speeding Up Transformers Inferencing. [folder]
- Working with AWS (Amazon Web Services).
operation research
reinforcement learning
ad
Notes related to advertising domain.
search
Information Retrieval, some examples are demonstrated using ElasticSearch.
time series
Forecasting methods for timeseries-based data.
- Getting started with time series analysis with Exponential Smoothing (Holt-Winters). [nbviewer][html]
- Framing time series problem as supervised-learning. [nbviewer][html]
- First Foray Into Discrete/Fast Fourier Transformation. [nbviewer][html]
projects
End to end project including data preprocessing, model building.
- Kaggle: Rossman Store Sales Predicting daily store sales. Also introduces deep learning for tabular data. [folder]
- Kaggle: Quora Insincere Questions Classification Predicting insincere questions. [folder]
ab tests
A/B testing, a.k.a experimental design. Includes: Quick review of necessary statistic concepts. Methods and workflow/thought-process for conducting the test and caveats to look out for.
- Frequentist A/B testing (includes a quick review of concepts such as p-value, confidence interval). [nbviewer][html]
- Quantile Regression and its application in A/B testing.
- Casual Inference
model selection
Methods for selecting, improving, evaluating models/algorithms.
- K-fold cross validation, grid/random search from scratch. [nbviewer][html]
- AUC (Area under the ROC curve and precision/recall curve) from scratch (includes the process of building a custom scikit-learn transformer). [nbviewer][html]
- Evaluation metrics for imbalanced dataset. [nbviewer][html]
- Detecting collinearity amongst features (Variance Inflation Factor for numeric features and Cramer’s V statistics for categorical features), also introduces Linear Regression from a Maximum Likelihood perspective and the R-squared evaluation metric. [nbviewer][html]
- Curated tips and tricks for technical and soft skills. [nbviewer][html]
- Partial Dependence Plot (PDP), model-agnostic approach for directional feature influence. [nbviewer][html]
- Kullback-Leibler (KL) Divergence. [nbviewer][html]
- Probability Calibration for classification models with Platt Scaling, Histogram Binning, Isotonic Regression. [nbviewer][html]
- Probability Calibration for deep learning classification models with Temperature Scaling. [nbviewer][html]
- HyperParameter Tuning with Ray Tune and Hyperband. [nbviewer][html]
dim reduct
Dimensionality reduction methods.
- Principal Component Analysis (PCA) from scratch. [nbviewer][html]
- Introduction to Singular Value Decomposition (SVD), also known as Latent Semantic Analysis/Indexing (LSA/LSI). [nbviewer][html]
recsys
Recommendation system with a focus on matrix factorization methods. Starters into the field should go through the first notebook to understand the basics of matrix factorization methods.
- Alternating Least Squares with Weighted Regularization (ALS-WR) from scratch. [nbviewer][html]
- ALS-WR for implicit feedback data from scratch & Mean Average Precision at k (mapk) and Normalized Cumulative Discounted Gain (ndcg) evaluation. [nbviewer][html]
- Bayesian Personalized Ranking (BPR) from scratch & AUC evaluation. [nbviewer][html]
- WARP (Weighted Approximate-Rank Pairwise) Loss using lightfm. [nbviewer][html]
- Factorization Machine from scratch. [nbviewer][html]
- Content-Based Recommenders:
- Approximate Nearest Neighborhood (ANN):
- Calibrated Recommendation for reducing bias/increasing diversity in recommendation. [nbviewer][html]
- Maximum Inner Product for Speeding Up Generating Recommendations. [nbviewer][html]
trees
Tree-based models for both regression and classification tasks.
- Decision Tree from scratch. [nbviewer][html]
- Random Forest from scratch and Extra Trees. [nbviewer][html]
- Gradient Boosting Machine (GBM) from scratch. [nbviewer][html]
- Xgboost API walkthrough (includes hyperparameter tuning via scikit-learn like API). [nbviewer][html]
- LightGBM API walkthrough and a discussion about categorical features in tree-based models. [nbviewer][html]
- Monotonic Constraint with Boosted Tree. [nbviewer][html]
clustering
TF-IDF and Topic Modeling are techniques specifically used for text analytics.
- TF-IDF (text frequency - inverse document frequency) from scratch. [nbviewer][html]
- K-means, K-means++ from scratch; Elbow method for choosing K. [nbviewer][html]
- Gaussian Mixture Model from scratch; AIC and BIC for choosing the number of Gaussians. [nbviewer][html]
- Topic Modeling with gensim’s Latent Dirichlet Allocation(LDA). [nbviewer][html]
keras
For those interested there’s also a keras cheatsheet that may come in handy.
- Multi-layers Neural Network (keras basics). [nbviewer][html]
- Multi-layers Neural Network hyperparameter tuning via scikit-learn like API. [nbviewer][html]
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN) - language modeling basics. [nbviewer][html]
- Text Classification
text classification
Deep learning techniques for text classification are categorized in its own section.
- Building intuition with spam classification using scikit-learn (scikit-learn hello world). [nbviewer][html]
- Bernoulli and Multinomial Naive Bayes from scratch. [nbviewer][html]
- Logistic Regression (stochastic gradient descent) from scratch. [nbviewer][html]
- Chi-square feature selection from scratch. [nbviewer][html]
regularization
Building intuition on Ridge and Lasso regularization using scikit-learn.
networkx
Graph library other than networkx are also discussed.
- PyCon 2016: Practical Network Analysis Made Simple. Quickstart to networkx’s API. Includes some basic graph plotting and algorithms. [nbviewer][html]
- Short Walkthrough of PageRank. [nbviewer][html]
- Influence Maximization from scratch. Includes discussion on Independent Cascade (IC), Submodular Optimization algorithms including Greedy and Lazy Greedy, a.k.a Cost Efficient Lazy Forward (CELF) [nbviewer][html]
association rule
Also known as market-basket analysis.
- Apriori from scratch. [nbviewer][html]
- Using R’s arules package (aprori) on tabular data. [Rmarkdown]
big data
Exploring big data tools, such as Spark and H2O.ai. For those interested there’s also a pyspark rdd cheatsheet and pyspark dataframe cheatsheet that may come in handy.
- Local Hadoop cluster installation on Mac. [markdown]
- PySpark installation on Mac. [markdown]
- Examples of manipulating with data (crimes data) and building a RandomForest model with PySpark MLlib. [nbviewer][html]
- PCA with PySpark MLlib. [nbviewer][html]
- Tuning Spark Partitions. [nbviewer][html]
- H2O API walkthrough (using GBM as an example). [nbviewer][html]
- Spark MLlib Binary Classification (using GBM as an example). [raw zeppelin notebook][Zepl]
data science is software
Best practices for doing data science in Python.
ga
Genetic Algorithm. Math-free explanation and code from scratch.
- Start from a simple optimization problem and extending it to traveling salesman problem (tsp).
- View [nbviewer][html]
unbalanced
Choosing the optimal cutoff value for logistic regression using cost-sensitive mistakes (meaning when the cost of misclassification might differ between the two classes) when your dataset consists of unbalanced binary classes. e.g. Majority of the data points in the dataset have a positive outcome, while few have negative, or vice versa. The notion can be extended to any other classification algorithm that can predict class’s probability, this documentation just uses logistic regression for illustration purpose.
- Visualize two by two standard confusion matrix and ROC curve with costs using ggplot2.
- View [Rmarkdown]
clustering old
A collection of scattered old clustering documents in R.
- Toy sample code of the LDA algorithm (gibbs sampling) and the topicmodels library. [Rmarkdown]
- k-shingle, Minhash and Locality Sensitive Hashing for solving the problem of finding textually similar documents. [Rmarkdown]
- Introducing tf-idf (term frequency-inverse document frequency), a text mining technique. Also uses it to perform text clustering via hierarchical clustering. [Rmarkdown]
- Some useful evaluations when working with hierarchical clustering and K-means clustering (K-means++ is used here). Including Calinski-Harabasz index for determine the right K (cluster number) for clustering and boostrap evaluation of the clustering result’s stability. [Rmarkdown]
linear regression
- Training Linear Regression with gradient descent in R, briefly covers the interpretation and visualization of linear regression’s summary output. [Rmarkdown]
Python Programming
- Extremely Quick Guide to Unicode. [markdown]
- Quick Example of Factory Design Pattern. [nbviewer][html]
- Parallel programming with Python (threading, multiprocessing, concurrent.futures, joblib). [nbviewer][html]
- Understanding iterables, iterator and generators. [nbviewer][html]
- Cohort analysis. Visualizing user retention by cohort with seaborn’s heatmap and illustrating pandas’s unstack. [nbviewer][html]
- Logging module. [nbviewer][html]
- Data structure, algorithms from scratch. [folder]
- Cython and Numba quickstart for high performance Python. [nbviewer][html]
- Optimizing Pandas (e.g. reduce memory usage using category type). [nbviewer][html]
- Unittest. [Python script]
- Using built-in data structure and algorithm. [nbviewer][html]
- Tricks with strings and text. [nbviewer][html]
- Python’s decorators (useful script for logging and timing function). [nbviewer][html]
- Pandas’s pivot table. [nbviewer][html]
- Quick introduction to classmethod, staticmethod and property. [nbviewer][html]