Archives
Follow the full collection chronologically, from the newest deep dives to the earliest foundations.
19 articles
Q3 Jul–Sep 6 articles
- Make the Agents Argue: Adversarial Review for Code That Cannot Fail Quietly ML Platform
- Prompt Caching: Paying Once for the Prefix that Every Request Repeats Deep Learning
- From Conversion Pixels to Incrementality: What Did the Ad Actually Cause? AdTech
- Inside Floor Price Optimization: Training the Bid-Threshold Classifiers AdTech
- Bid Landscaping, Part 2: From Win Curve to Bidding, Pacing, and Its Limits AdTech
- Bid Landscaping, Part 1: Learning the Win Curve from the Auctions We Lost AdTech
Q2 Apr–Jun 5 articles
- Training LLMs on a Budget: LoRA, QLoRA, and LoftQ Deep Learning
- Same Ruler, Smarter Placement: GPTQ Deep Learning
- Slope and Curvature: A Practical Guide to the Jacobian and Hessian Deep Learning
- Same Ruler, Smarter Placement: AWQ Deep Learning
- Packing Intelligence into Fewer Bits: Non-Linear Quantization in LLMs Deep Learning
Q1 Jan–Mar 8 articles
- A Practical Introduction to LLM Quantization and Linear Mapping Deep Learning
- Epilogue: The Common Thread Behind A/B Testing (Enter the GLM) Statistics
- Contextual Bandits: Personalization with LinUCB and Linear Thompson Sampling Statistics
- Beyond A/B Testing: Thompson Sampling and UCB Statistics
- Beyond A/B Testing: Causal Inference in the Wild (DiD, PSM, and IV) Statistics
- Decoding RAG Evaluation: When Your Pipeline Fails, Who is to Blame? Deep Learning
- KV Cache: The Trick That Lets LLMs Remember Without Recomputing Deep Learning
- Demystifying LLM Temperature: The Math Behind the Magic of Token Sampling Deep Learning
55 articles
Q4 Oct–Dec 55 articles
- Introduction to AdTech: The Post-Cookie Frontier - Identity, Privacy, and What Comes Next AdTech
- Introduction to AdTech: The Intelligence Layer - Machine Learning in the Millisecond AdTech
- Introduction to AdTech: The Millisecond Handshake - Ad Tags, Pixels, and the Redirect Loop AdTech
- Introduction to AdTech: Who Decides Which Ad Gets Served? AdTech
- Introduction to AdTech: The Invisible Auction and its Participants AdTech
- Deep Learning Primer: Understanding Word Embeddings: From SVD to Word2Vec Deep Learning
- Deep Learning Primer: RNN & LSTM Deep Learning
- Deep Learning Primer: Exploring Regularization Techniques in DNN Deep Learning
- Deep Learning Primer: Weight Initialization and Layer Normalization Deep Learning
- Deep Learning Primer: A concise introduction to Backprop and Optimizers Deep Learning
- Deep Learning Primer: Diving into the Activation Function Pool Deep Learning
- Storage for ML Systems, Part 2: Bigtable and Spanner Deep Dives ML Platform
- Storage for ML Systems, Part 1: Choosing the Right Store ML Platform
- From Boring to Brilliant: A Guide to LLM Sampling Techniques Deep Learning
- Boosting, Part 3: Choosing a Library (XGBoost, LightGBM, CatBoost) Machine Learning
- Boosting, Part 2: The XGBoost Mathematics Machine Learning
- Boosting, Part 1: AdaBoost and Gradient Boosting Machine Learning
- The Wisdom of Trees, Part 2: Random Forests Machine Learning
- The Wisdom of Trees, Part 1: Decision Trees Machine Learning
- Understanding PCA Once and for All Machine Learning
- Connection between KL Divergence & Cross Entropy Machine Learning
- A Short Primer on KL/JS Divergence Machine Learning
- Venturing into the Isolation Forest Machine Learning
- Interpretations of ROC-AUC & PR-AUC metrics Machine Learning
- Support Vector Machines, Part 4: One-Class SVM for Novelty Detection Machine Learning
- Support Vector Machines, Part 3: The Kernel Trick Machine Learning
- Support Vector Machines, Part 2: The Dual and Support Vectors Machine Learning
- Support Vector Machines, Part 1: Geometry, Margins, and Hinge Loss Machine Learning
- Naive Bayes: From Bayes' Rule to Text Classification Machine Learning
- Logistic Regression, Part 3: Softmax, Neural Networks, and Imbalanced Data Machine Learning
- Logistic Regression, Part 2: Coefficients, Decision Boundaries, and Limitations Machine Learning
- Logistic Regression, Part 1: From Log-Odds to Gradient Descent Machine Learning
- Bayesian Linear Regression: Where Ridge and Lasso Come From Statistics
- Linear Regression, Part 3: Evaluation, Interpretation, and Extensions Machine Learning
- Linear Regression, Part 2: Multicollinearity and Regularization Machine Learning
- Linear Regression, Part 1: OLS Foundations Machine Learning
- Understanding Bias-Variance Tradeoff Machine Learning
- Missing Data Imputation: From Mechanisms to a Leakage-Safe Workflow Machine Learning
- Stats Primer: How to Choose the Right Sample Size for Your A/B Tests Statistics
- Stats Primer: Non-Parametric A/B Tests Statistics
- Stats Primer: Parametric A/B Tests Statistics
- Stats Primer: Designing a Trustworthy A/B Test Statistics
- Stats Primer: A Detailed Look at z & t-statistic Statistics
- Stats Primer: P-values & Confidence Intervals Statistics
- Probability Distribution: Beta Probability
- Probability Distribution: Gamma Probability
- Probability Distribution: Laplace Probability
- Probability Distribution: Log-Normal Probability
- Probability Distribution: Normal (Gaussian) Probability
- Probability Distribution: Negative Binomial Probability
- Probability Distribution: Geometric Probability
- Probability Distribution: Exponential Probability
- Probability Distribution: Poisson Probability
- Probability Distribution: Binomial Probability
- Probability Distributions: A Field Guide Probability