Machine Learning
Deep dives into ML algorithms, models, and applications
Computer Vision for Software Developers: What You Can Build Today
Using pretrained models for classification, detection, OCR, and segmentation. APIs vs local inference. When to fine-tune vs use a multimodal LLM. CLIP for image search.
Cross-Validation: Reliably Estimating Model Performance on Unseen Data
A single train/test split gives you a noisy estimate of real performance. Cross-validation gives you a reliable one. Here is every variant, when to use each, and the mistakes to avoid.
Generative vs Discriminative Models: The Fundamental ML Distinction
Discriminative models learn decision boundaries. Generative models learn data distributions. Understanding this split explains why LLMs can generate text and when each approach wins.
Data Labeling Guide: How to Annotate Training Data Efficiently and Accurately
Labeling tools, quality control with inter-annotator agreement, active learning to cut labeling costs by 60-80%, and programmatic labeling with Snorkel.
Auditing ML Models for Bias: A Practical Guide
ML bias is systematic, measurable, and addressable. This guide covers the types of bias, fairness metrics, audit process, and tools to find and fix disparate model performance.
A/B Testing ML Models in Production: What's Different and How to Do It Right
Why ML A/B tests differ from standard software tests, novelty effects, multi-armed bandits, shadow mode testing, and how to measure model impact rigorously.
Hyperparameter Tuning and Optuna Guide for Machine Learning
Master machine learning hyperparameter tuning with Optuna, Bayesian optimization, Tree-structured Parzen Estimators, MedianPruner, and distributed PostgreSQL studies.
Gradient Descent Explained: How Machine Learning Models Actually Learn
Gradient descent is the engine behind every modern ML model. Here is how it works, why learning rate matters, and when to use Adam over SGD.
BERT Explained for Developers: What It Is, How It Works, and When to Use It
BERT introduced bidirectional context to NLP in 2018. Here is what that means, how it differs from GPT, and when to reach for it over a modern LLM API.
Time Series Forecasting: ARIMA, LightGBM, and LSTM Compared
Time series has seasonality, trend, and temporal dependencies that standard ML ignores. Here is when to use ARIMA vs. LightGBM lag features vs. LSTM — and the critical mistake of random data splits.
Natural Language Inference: The Technique Behind Zero-Shot Text Classification
NLI models can classify text into any category without labeled examples. Here is how entailment-based classification works, the best models to use, and real-world limitations.
Building a Dataset for ML: What Makes Good Training Data
Data quantity requirements, labeling strategies, augmentation techniques, the data flywheel, and how production models generate their own training data.
LLM Fine-Tuning in Practice: A Developer's Complete Walkthrough
Dataset preparation, LoRA hyperparameters, Unsloth for faster training, evaluation against the base model, and avoiding catastrophic forgetting and overfitting.
GPT Architecture Explained: Beyond the Surface Level
GPT's autoregressive, decoder-only design enables text generation at scale. Here is how it actually works -- from pretraining data to emergent capabilities to GPT-4o.
Neural Networks Explained: A Visual Guide for Software Developers
A neural network is layers of mathematical functions that transform inputs into outputs. Here is how they work, why depth matters, and what developers need to know.
MLflow Experiment Tracking and LLM Tracing Guide in Python
Master MLflow for experiment tracking, model registry, and LLM tracing in Python. Learn self-hosted PostgreSQL setups, autologging, and production deployment.
Overfitting and Underfitting in ML: How to Diagnose and Fix Both
Overfitting memorizes training data and fails on new data. Underfitting is too simple to capture patterns. The train vs. validation loss curve tells you which you have. Learn to diagnose and fix both with dropout, regularization, early stopping, and more.
Building a RAG System From Scratch: A Complete Implementation Guide
RAG retrieves relevant documents at query time and adds them to the prompt. Five steps: chunk, embed, store, retrieve, evaluate. Here is the complete implementation.
LayoutLMv3: Understanding PDFs, Forms, and Documents With Layout Awareness
Microsoft's LayoutLMv3 pretrains on text, bounding boxes, and image patches together, enabling form understanding, receipt parsing, and document VQA without separate OCR fine-tuning.
When Not to Use Machine Learning: Simpler Solutions That Actually Work
Most ML projects fail not because ML is hard but because ML was the wrong tool. A decision tree, a regex, or a database query solves most 'AI' problems faster.
Vector Databases Explained: What They Are and When to Use Them
A vector database stores embeddings and finds the most similar ones to a query. SQL cannot do this. ChromaDB, Pinecone, Weaviate, pgvector, and Qdrant compared.
Neptune.ai: Experiment Tracking for ML Teams That Outgrew Notebooks
Neptune.ai tracks ML experiments, stores artifacts and metrics, and enables team collaboration on model comparisons - bridging the gap between prototype notebooks and production model management.
DeepSpeed: Train Models With Billions of Parameters on Limited GPUs
Microsoft's DeepSpeed enables training of 100B+ parameter models across distributed GPU clusters through ZeRO optimization stages, CPU offloading, and RLHF support.
PEFT and LoRA: Fine-Tune a 7B LLM on a Single GPU in 2026
The PEFT library with LoRA and QLoRA enables fine-tuning 7B parameter LLMs on a single consumer GPU by updating only a small fraction of parameters, reducing VRAM from 14GB to under 5GB.
