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Insights on AI, Machine Learning, Web Development, and emerging technologies from industry experts.
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133–144 of 528
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.
Mahmudul Haque Qudrati
CEO & ML Engineer
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
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
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
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
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.
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
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.
Mahmudul Haque Qudrati
CEO & ML Engineer
Discriminative models learn decision boundaries. Generative models learn data distributions. Understanding this split explains why LLMs can generate text and when each approach wins.
Mahmudul Haque Qudrati
CEO & ML Engineer
Users abandon features above 300ms. Here is the complete playbook for hitting production latency targets: quantization, batching, caching, hardware selection, and pre-computation.
Mahmudul Haque Qudrati
CEO & ML Engineer
From Attention Is All You Need to DeepSeek V3 — these are the papers that shaped modern ML. How to read them efficiently and what each one actually contributed.
Mahmudul Haque Qudrati
CEO & ML Engineer