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
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
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
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
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.
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
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
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
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
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
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
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
Overfitting memorizes training data and fails on new data. Underfitting is too simple to capture patterns. The train vs. validation loss curve tells you which you have. Learn to diagnose and fix both with dropout, regularization, early stopping, and more.
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