Category
Machine Learning
Deep dives into ML algorithms, models, and applications
// 12 articles filed
// FIG. CAT-MACH
PristrenPristrenDeep dives into ML algorithms, models, and applications
// 12 articles filed
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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.
Mahmudul Haque Qudrati
CEO & ML Engineer
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.
Mahmudul Haque Qudrati
CEO & ML Engineer
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.
Mahmudul Haque Qudrati
CEO & ML Engineer
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.
Mahmudul Haque Qudrati
CEO & ML Engineer
Data quantity requirements, labeling strategies, augmentation techniques, the data flywheel, and how production models generate their own training data.
Mahmudul Haque Qudrati
CEO & ML Engineer
A full practical walkthrough of training a neural network - data prep, architecture selection, optimizer config, common failure modes, and getting to production.
Mahmudul Haque Qudrati
CEO & ML Engineer
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.
Mahmudul Haque Qudrati
CEO & ML Engineer
Dataset preparation, LoRA hyperparameters, Unsloth for faster training, evaluation against the base model, and avoiding catastrophic forgetting and overfitting.
Mahmudul Haque Qudrati
CEO & ML Engineer
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.
Mahmudul Haque Qudrati
CEO & ML Engineer
Why models degrade, what to monitor, detecting drift with statistical tests, automated retraining triggers, and tools like Evidently AI and Arize.
Mahmudul Haque Qudrati
CEO & ML Engineer
Labeling tools, quality control with inter-annotator agreement, active learning to cut labeling costs by 60-80%, and programmatic labeling with Snorkel.
Mahmudul Haque Qudrati
CEO & ML Engineer
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.
Mahmudul Haque Qudrati
CEO & ML Engineer
AI trends, techniques, and real-world implementations
How LLMs work, honest comparisons, and production usage
Every technique that works — with real examples
Claude Code, Cursor, Copilot, open-source tools reviewed honestly
Local LLMs, open models, free AI infrastructure
Fewer tokens, cheaper APIs, local alternatives with real numbers
Benchmarks explained, evaluation frameworks, model testing
LLM SEO, AI SEO, Google AI Overviews, developer marketing
iOS, Android, and cross-platform mobile app development
Modern web technologies, frameworks, and best practices
Data analysis, visualization, and engineering insights
Autonomous agents, LLM applications, and intelligent systems