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
StableLM 2 1.6B outperforms Phi-1.5 and TinyLlama at its size class and is small enough to run on a Raspberry Pi, in a browser via WebLLM, or on old consumer hardware.
Mahmudul Haque Qudrati
CEO & ML Engineer
DINOv2 learns visual features from 142 million curated images without labels, producing representations that outperform supervised ImageNet models as frozen feature extractors across classification, segmentation, and depth tasks.
Mahmudul Haque Qudrati
CEO & ML Engineer
Evidently AI generates data drift reports, quality checks, and model performance dashboards for production ML - catching distribution shifts before they silently corrupt your predictions.
Mahmudul Haque Qudrati
CEO & ML Engineer
BLIP-2 bridges a frozen CLIP image encoder and a frozen LLM through a lightweight Q-Former, achieving strong VQA and captioning performance without updating the large pretrained components.
Mahmudul Haque Qudrati
CEO & ML Engineer
PyTorch Lightning separates research code from engineering code - write your model logic once and get multi-GPU, mixed precision, gradient clipping, and logging for free.
Mahmudul Haque Qudrati
CEO & ML Engineer
Wav2Vec 2.0 learns speech representations from unlabeled audio and can be fine-tuned with as little as 10 minutes of transcribed speech, making high-quality ASR accessible for low-resource languages.
Mahmudul Haque Qudrati
CEO & ML Engineer
Optuna uses Tree-structured Parzen Estimators to learn from previous trials and focus on promising regions - finding better hyperparameters in fewer trials than grid or random search.
Mahmudul Haque Qudrati
CEO & ML Engineer
Quantization shrinks LLM weights from float32 to int4 or int8 - here is exactly what each GGUF level means, how memory usage scales, and the quality tradeoffs.
Mahmudul Haque Qudrati
CEO & ML Engineer
T5 unifies all NLP tasks as sequence-to-sequence text generation, and Flan-T5 extends this with instruction tuning across 1800+ tasks, making it a practical base for fine-tuning custom generation tasks.
Mahmudul Haque Qudrati
CEO & ML Engineer
All three gradient boosting libraries beat neural networks on tabular data - but they differ in training speed, categorical handling, and GPU support in ways that matter for your specific use case.
Mahmudul Haque Qudrati
CEO & ML Engineer
NLLB-200 provides machine translation for 200 languages including 55 low-resource African languages, with a distilled 600M model that outperforms Google Translate on 40+ languages. Practical guide with code examples and cost comparison.
Mahmudul Haque Qudrati
CEO & ML Engineer
Scikit-learn remains the best library for classical ML on tabular data - v1.4+ adds HDBSCAN, TunedThresholdClassifierCV, and better Pipeline verbosity while staying beginner-friendly.
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