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
Built by Pristren
Reading about AI tools? Run your team on Zlyqor — chat, meetings, projects, and time tracking in one workspace.
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.
Mahmudul Haque Qudrati
CEO & ML Engineer
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.
Mahmudul Haque Qudrati
CEO & ML Engineer
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.
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
Bagging, boosting, and stacking -- ensemble methods consistently win Kaggle competitions and improve production accuracy. Here is how each works and when to use them.
Mahmudul Haque Qudrati
CEO & ML Engineer
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.
Mahmudul Haque Qudrati
CEO & ML Engineer
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.
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
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
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.
Mahmudul Haque Qudrati
CEO & ML Engineer
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.
Mahmudul Haque Qudrati
CEO & ML Engineer
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.
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