Machine Learning
Deep dives into ML algorithms, models, and applications
11 min readConvolutional Neural Networks: How Computers Learn to See
CNNs use convolutions to detect local patterns in images. Pooling downsamples. ResNet residual connections solve vanishing gradients. Here is when to train from scratch vs. use a pretrained model.
9 min readHyperparameter Tuning: Finding the Model Settings That Actually Matter
Learning rate, batch size, regularization -- the right hyperparameters can mean 10+ percentage points of accuracy. Here is how to find them efficiently without exhaustive search.
9 min readAuditing 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.
8 min readHow Product Teams Can Work Effectively With Machine Learning
What ML can and cannot do for your product, how to write an ML spec, how to evaluate model readiness, and what PMs consistently get wrong working with data scientists.
9 min readEnsemble Methods: Why Combining Models Beats Any Individual Model
Bagging, boosting, and stacking -- ensemble methods consistently win Kaggle competitions and improve production accuracy. Here is how each works and when to use them.
10 min readDecision Trees and Random Forests Explained: When Tree Methods Beat Neural Networks
Decision trees overfit easily. Random forests fix this by averaging many trees. XGBoost pushes further with gradient boosting. Here is when tree methods beat neural networks.
9 min readGenerative 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.
9 min readA/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.
10 min readSupervised Learning Explained: How Models Learn from Labeled Examples
Supervised learning is the most widely used ML paradigm. Here is exactly how the train-measure-adjust loop works, where labels come from, and when the approach breaks down.
7 min readTime 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.
9 min readKnowledge Distillation: Training Small Models to Match Large Ones
Knowledge distillation lets you deploy fast, small models that match the performance of large ones. Here is how it works, why soft targets help, and when to use it in production.
9 min readWhen 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.
7 min readLayoutLMv3: 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.
11 min readBuilding 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.
10 min readNeural 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.
7 min readOverfitting 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.
10 min readVector 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.
4 min readNeptune.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.
4 min readPEFT 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.
8 min readDeepSpeed: 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.
7 min readMusicGen: Meta's Text-to-Music Model That Runs Locally
Meta's MusicGen generates 30-second music clips from text descriptions or melody conditioning using an EnCodec audio tokenizer and autoregressive transformer - fully open and self-hostable.
8 min readLLM Compression: Pruning, Distillation, and Quantization Compared
Three techniques for making large language models smaller and faster - quantization, pruning, and knowledge distillation - each with different tradeoffs in quality, speed, and implementation complexity.
7 min readSPLADE: Sparse Neural Retrieval That Beats BM25 With Learned Weights
SPLADE uses BERT's masked language model head to produce sparse, interpretable retrieval representations that outperform BM25 while remaining compatible with inverted index infrastructure.
7 min readNomic Embed: The First Fully Open 8192-Context Embedding Model
nomic-embed-text-v1.5 supports 8192-token context with Matryoshka embeddings at multiple dimensions - fully open training data, code, and weights under Apache 2.0.