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
A neural network is layers of mathematical functions that transform inputs into outputs. Here is how they work, why depth matters, and what developers need to know.
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
Microsoft's LayoutLMv3 pretrains on text, bounding boxes, and image patches together, enabling form understanding, receipt parsing, and document VQA without separate OCR fine-tuning.
Mahmudul Haque Qudrati
CEO & ML Engineer
RAG retrieves relevant documents at query time and adds them to the prompt. Five steps: chunk, embed, store, retrieve, evaluate. Here is the complete implementation.
Mahmudul Haque Qudrati
CEO & ML Engineer
Neptune.ai tracks ML experiments, stores artifacts and metrics, and enables team collaboration on model comparisons - bridging the gap between prototype notebooks and production model management.
Mahmudul Haque Qudrati
CEO & ML Engineer
A vector database stores embeddings and finds the most similar ones to a query. SQL cannot do this. ChromaDB, Pinecone, Weaviate, pgvector, and Qdrant compared.
Mahmudul Haque Qudrati
CEO & ML Engineer
The PEFT library with LoRA and QLoRA enables fine-tuning 7B parameter LLMs on a single consumer GPU by updating only a small fraction of parameters, reducing VRAM from 14GB to under 5GB.
Mahmudul Haque Qudrati
CEO & ML Engineer
Microsoft's DeepSpeed enables training of 100B+ parameter models across distributed GPU clusters through ZeRO optimization stages, CPU offloading, and RLHF support.
Mahmudul Haque Qudrati
CEO & ML Engineer
Meta's MusicGen generates 30-second music clips from text descriptions or melody conditioning using an EnCodec audio tokenizer and autoregressive transformer - fully open and self-hostable.
Mahmudul Haque Qudrati
CEO & ML Engineer
Three techniques for making large language models smaller and faster - quantization, pruning, and knowledge distillation - each with different tradeoffs in quality, speed, and implementation complexity.
Mahmudul Haque Qudrati
CEO & ML Engineer
SPLADE uses BERT's masked language model head to produce sparse, interpretable retrieval representations that outperform BM25 while remaining compatible with inverted index infrastructure.
Mahmudul Haque Qudrati
CEO & ML Engineer
nomic-embed-text-v1.5 supports 8192-token context with Matryoshka embeddings at multiple dimensions - fully open training data, code, and weights under Apache 2.0.
Mahmudul Haque Qudrati
CEO & ML Engineer
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