Tag: #machine-learning
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 14 posts
14 posts are tagged #machine-learning; the most recent was published on 2026-08-14.
Often tagged together: #2026-03 8 #ai 5 #deep-learning 3 #calculus 2 #cuda 2
Most read in the last 90 days:
The HEIR Homomorphic Encryption Compiler and the Condition Called Cost — When the Comments Supply the Number the Announcement Omits
Google published HEIR, an open source compiler toolchain for homomorphic encryption. The real news here is the compiler, not the cryptography — what has kept homomorphic encryption out of deployment was never the mathema
2026-08-14 · 7 min read #cryptography#privacy#compiler#machine-learning#securityDesigning a Credit Scoring System (CSS) — From Scorecards to ML Underwriting
A deep dive into the CSS (Credit Scoring System), the brain of loan underwriting. We cover application vs behavior scores, WoE/IV-based scorecard development, the regulatory comparison between logistic regression and ML
2026-06-13 · 16 min read #credit-scoring#css#risk-management#machine-learning#scorecardDiffusion Models Deep Dive — DDPM, Latent Diffusion, Classifier-Free Guidance, DDIM, Stable Diffusion Complete Guide (2025) ♪ Listenable
Diffusion models — the foundation of Stable Diffusion, DALL-E, Midjourney, and Sora. This post dissects diffusion models from scratch. Forward/Reverse diffusion process, DDPM and the variational lower bound, the score-ba
2026-04-15 · 25 min read #diffusion#generative-ai#stable-diffusion#ddpm#machine-learningCUDA GPU Programming Model Deep Dive — SIMT, Memory Hierarchy, Tensor Core, Kernel Optimization (2025)
The engine behind ChatGPT, Stable Diffusion, and Sora — NVIDIA GPUs and CUDA. This post dissects the CUDA programming model from scratch: GPU hardware architecture (SM, Warp, CUDA Core), SIMT vs SIMD, thread hierarchy (G
2026-04-15 · 15 min read #cuda#gpu#nvidia#ai#machine-learningPython Complete Guide for AI/ML: Master NumPy, Pandas, Matplotlib, and Scikit-learn ♪ Listenable
The complete guide to mastering the Python ecosystem for AI and machine learning. Master NumPy array operations, Pandas data processing, Matplotlib/Seaborn visualization, and Scikit-learn machine learning through hands-o
2026-03-17 · 22 min read #python#numpy#pandas#scikit-learn#matplotlibMathematical Foundations for AI/ML: Complete Guide - Linear Algebra, Calculus, Probability ♪ Listenable
A complete guide to mastering the core mathematics required to understand AI and machine learning. Covers linear algebra (vectors, matrices, eigenvalues), calculus (partial derivatives, chain rule), probability and stati
2026-03-17 · 21 min read #mathematics#linear-algebra#calculus#probability#statisticsData-Centric AI Complete Guide: Maximizing AI Performance with High-Quality Data ♪ Listenable
A complete guide to mastering Data-Centric AI. Learn data quality measurement, label refinement, data augmentation, synthetic data generation, Cleanlab, active learning, and the data flywheel, all with hands-on code exam
2026-03-17 · 20 min read #data-centric-ai#data-quality#data-labeling#synthetic-data#aiAutoML Complete Guide: Automated ML Pipelines with AutoGluon, FLAML, and Optuna
A complete guide to mastering AutoML. Learn hyperparameter optimization (Optuna, Ray Tune), AutoGluon, FLAML, H2O AutoML, neural architecture search (NAS), and how AutoML is transforming data science — all with hands-on
2026-03-17 · 17 min read #automl#autogluon#optuna#hyperparameter-optimization#neural-architecture-searchTabular Data ML Complete Guide: Master XGBoost, LightGBM, CatBoost, and TabNet
A complete guide to achieving top performance on tabular data. Master XGBoost, LightGBM, CatBoost ensembles, feature engineering, hyperparameter optimization, and deep learning-based TabNet with Kaggle-level practical te
2026-03-17 · 18 min read #tabular-ml#xgboost#lightgbm#catboost#gradient-boostingAIOps-based Anomaly Detection Automation: ML Alerting and Kubernetes Event Correlation Guide ♪ Listenable
A comprehensive guide to AIOps-based anomaly detection automation. Covers limitations of traditional threshold alerting, ML-based anomaly detection algorithms (Isolation Forest, Prophet, DBSCAN), Prometheus metric-based
2026-03-09 · 20 min read #observability#aiops#anomaly-detection#machine-learning#kubernetesMath + LaTeX/KaTeX Complete Guide for Reading AI/ML Papers ♪ Listenable
A comprehensive summary of essential math concepts (linear algebra, calculus, probability/statistics, optimization) and LaTeX/KaTeX formula syntax for reading AI/ML papers with practical examples, symbol cheat sheets, an
2026-03-08 · 20 min read #ai-papers#math#latex#katex#linear-algebraMLflow Production Guide: Experiment Tracking, Model Registry, and Scalable MLOps Workflow
A comprehensive guide to MLflow covering experiment tracking at scale, model registry lifecycle management, CI/CD integration, PostgreSQL and S3 backend configuration, multi-team collaboration, and production deployment
2026-03-07 · 15 min read #ai-platform#mlflow#experiment-tracking#model-registry#mlopsMLOps Feature Store in Practice — Building a Feature Pipeline with Feast
Build an offline/online feature store with Feast and create a production-grade pipeline that serves consistent features for both training and serving
2026-03-02 · 9 min read #mlops#feast#feature-store#machine-learning#data-engineeringCUDA Hands-on Complete Guide: Everything About GPU Computing ♪ Listenable
A comprehensive guide covering NVIDIA CUDA from fundamental concepts to GPU architecture, C/C++ kernel programming, memory optimization, Python integration (PyTorch, Numba, CuPy), Multi-GPU training, profiling, and troub
2026-03-01 · 51 min read #cuda#gpu#nvidia#deep-learning#machine-learning