Results for “mdn”

Comprehensive guide to mpnn. Master the concepts, implementation, best practices, and real-world applications of mpnn in professional environments.

Manage multiple n8n instances over MCP by discovering, switching, and verifying the target instance before reads and writes.

Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data, covering correlation testing, available recipes, and multi-GPU examples.

Provides practical guidance for training Mixture-of-Experts Vision-Language Models in Megatron Bridge, comparing FSDP and 3D-parallel approaches with lessons from recent multimodal experiments.

Convert Markdown files to professionally formatted Word (.docx) documents with embedded PNG images using pure JavaScript.

Migrates .NET test projects from VSTest to Microsoft.Testing.Platform (MTP), updating project files, CLI arguments, and CI/CD pipelines for MSTest, NUnit, and xUnit.net.

Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.

Comprehensive guide to mdp. Master the concepts, implementation, best practices, and real-world applications of mdp in professional environments.

Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.

Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces).

Provides expert guidance for using n8n-mcp MCP server tools to build, validate, and manage n8n workflows efficiently.

Configure, optimize, and troubleshoot CDN deployments with caching strategies, security hardening, and multi-provider management.

Search personal knowledge bases, notes, docs, and meeting transcripts locally using qmd — a hybrid retrieval engine with BM25, vector search, and LLM reranking. Supports CLI and MCP integration.

Search local knowledge bases, notes, docs, and meeting transcripts with hybrid retrieval combining BM25, vector search, and LLM reranking, all running on-device.

Mcp Vods

Converts a markdown deck into a single-file HTML presentation with keyboard navigation, presenter mode, and print-to-PDF support.

Decide when and how to assess minimal residual disease (MRD) in multiple myeloma — bone marrow MRD by next-generation flow (NGF) or sequencing (NGS), plus imaging MRD by PET-CT — and how to act on the result. Trigger when a clinician asks "how to assess MRD in myeloma", "MRD-negative complete response", "PET-CT for MRD", "is bone marrow MRD enough", "when to test for MRD", "next-generation flow for myeloma", or "myeloma sustained MRD negativity". Based on EHA-ESMO 2021.

Comprehensive guide to dqn. Master the concepts, implementation, best practices, and real-world applications of dqn in professional environments.

PDN DC voltage/current analysis, IR drop, design rule checking, and multi-net batch analysis on imported PCB layouts. TRIGGER: user asks about power integrity, PDN analysis, IR drop, voltage distribution, current density, power nets, or design rule checking on a PCB. Invoke BEFORE writing code — the PDN API chain is specialized and non-obvious. SKIP: importing a PCB file (use matlab-read-pcb-layout), EM field/S-parameter extraction (use matlab-analyze-em), material/stackup setup only (use matlab-manage-pcb-material), transmission line design (use matlab-design-pcb-transmission-line).

Design CDN strategies for content delivery. TRIGGERS - Use when user needs help with cdn-strategy related tasks.

Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug discovery ML.

Roadmap parsing, analysis, and mutation operations for ROADMAP.md. Handles phase and milestone lifecycle including add, insert (decimal), remove, complete, and requirements coverage analysis.

Search personal knowledge bases, notes, docs, and meeting transcripts locally using qmd — a hybrid retrieval engine with BM25, vector search, and LLM reranking. Supports CLI and MCP integration.

Design CDN strategies for content delivery. TRIGGERS - Use when user needs help with cdn-strategy related tasks.

Provides guidance for training Mixture-of-Experts models with long context windows, covering context parallelism sizing, selective recomputation, dispatcher choices, and practical patterns from recent experiments.

Plan, debug, tune, evaluate, export, or deploy public Nemotron embedding and reranking retrieval recipes using the current checkout.

Finetunes the NV-Generate-CTMR MR-brain diffusion UNet from user-supplied NIfTI training volumes using a wrapper that stages configs and delegates to upstream scripts.

Configure fault tolerance, straggler detection, preemption, in-process restart, and re-run state machine for Megatron Bridge training jobs.

Installs and configures MariaDB Connector/ODBC, covering driver manager registration, DSN setup, connection-string keywords, and TLS options for Linux, macOS, and Windows.

Converts long-form markdown documents (specs, RFCs, reports, plans) into a single-file HTML page with sticky table of contents, scrollspy, search filter, code-copy buttons, and customizable design tokens.

Machine learning development agent for end-to-end ML workflows: data preprocessing, model training, evaluation, hyperparameter tuning, and deployment

Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.

Provides guidance on Hong Kong's four mobile network operators for reference and selection.

Perform nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows using the umap-learn library.

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.