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
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.
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
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
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
How semantic search works, embedding-based architecture, pgvector vs ChromaDB, hybrid search with BM25, and cross-encoder re-ranking for better results.
Mahmudul Haque Qudrati
CEO & ML Engineer
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
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
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
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
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.
Mahmudul Haque Qudrati
CEO & ML Engineer
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.
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