Machine Learning
Deep dives into ML algorithms, models, and applications
Machine Learning: Complete Guide for Software Developers
Learn machine learning as a software developer with this complete guide covering Python, algorithms, mathematics, projects, deep learning, and a practical roadmap.
ONNX: Export Any ML Model and Run It Anywhere
ONNX (Open Neural Network Exchange) is the universal model format - export from PyTorch, scikit-learn, or HuggingFace and run 3x faster inference with ONNX Runtime on CPU or GPU.
Decision 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.
Anomaly Detection: Finding Outliers Without Labels
Anomaly detection finds rare events without labeled examples. Here is how Isolation Forest, One-Class SVM, and Autoencoders work -- and why accuracy is the wrong metric.
ML Model Compression: Pruning, Quantization, and Knowledge Distillation
How to make models smaller and faster for production using pruning, quantization (FP32 to INT8), knowledge distillation, ONNX conversion, and when full precision is necessary.
ML Monitoring in Production: Detecting Data Drift Before It Breaks Your Model
Why models degrade, what to monitor, detecting drift with statistical tests, automated retraining triggers, and tools like Evidently AI and Arize.
Supervised 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.
Convolutional 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.
Building Semantic Search: Finding Results by Meaning, Not Keywords
How semantic search works, embedding-based architecture, pgvector vs ChromaDB, hybrid search with BM25, and cross-encoder re-ranking for better results.
Reinforcement Learning for Software Developers: A Practical Guide
What RL is, where it works, where it fails, RLHF for LLM alignment, and how to decide between RL and supervised learning for your specific problem.
Transfer Learning Explained: Reusing What Neural Networks Already Know
Transfer learning lets you start from a pretrained model instead of random weights. Here is why it works, when to fine-tune vs. freeze layers, and when it fails.
How 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.
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.
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.
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.
ML Deployment Patterns: From REST API to Edge Inference
REST APIs, batch inference, streaming, edge deployment, model serving frameworks, and canary deployments for safely rolling out new model versions.
Feature Engineering: The Practical Guide to Transforming Raw Data into ML Inputs
Feature engineering is where most ML project time actually goes. Here is how to do log transforms, one-hot encoding, cyclical encoding, and interaction features that move the needle.
ML Model Evaluation Metrics: Why Accuracy Lies and What to Use Instead
Accuracy is misleading on imbalanced datasets. Here is when to use precision, recall, F1, AUC-ROC, MAE, RMSE, and how to choose the right metric for your problem.
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.
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.
The Complete Neural Network Training Guide: From Data to Deployed Model
A full practical walkthrough of training a neural network - data prep, architecture selection, optimizer config, common failure modes, and getting to production.
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.
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.
Dimensionality Reduction: PCA, t-SNE, and UMAP Explained
High-dimensional data is hard to work with. PCA, t-SNE, and UMAP each reduce it differently. Here is when to use each and how to avoid the curse of dimensionality.
