Results for “deepgram”

Process and analyze high-throughput sequencing data with deepTools for quality control, normalization, comparison, and publication-quality visualizations of ChIP-seq, RNA-seq, and ATAC-seq experiments.

Predict molecular properties, train graph neural networks, and run drug discovery workflows using DeepChem's featurizers, models, and MoleculeNet benchmarks.

Socratic deep interview with mathematical ambiguity gating before explicit execution approval

Implements deep linking, universal links (iOS), and app links (Android) in Flutter using go_router, including domain configuration, file association, and URL-based dynamic routing.

NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.

PyTorch, TensorFlow, neural networks, CNNs, transformers, and deep learning for production

Imported skill graph from langchain

Builds and trains deep learning models with PyTorch, including tensors, autograd, and neural network modules.

Queries the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biomedical relationships across genes, drugs, diseases, phenotypes, pathways, and biological processes. Use when exploring drug-disease or gene-disease links, building disease-centric knowledge subgraphs, or sourcing relations for drug repurposing and precision-medicine analyses. Part of the AlterLab Academic Skills suite.

Detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using the Resemble AI platform.

Use ddgr (DuckDuckGo from the terminal) to perform privacy-focused web searches from the command line. Use when the user wants to (1) Search the web from the terminal, (2) Perform DuckDuckGo searches without opening a browser, (3) Get quick search results in text format, (4) Search privately without tracking, (5) Use DuckDuckGo bangs (!) from terminal.

Design, select, and analyze waveforms for radar, sonar, and active sensing using the Phased Array System Toolbox. Covers LFM, NLFM, FMCW, phase-coded, CW, stepped FM, custom IQ, ambiguity functions, sidelobe reduction, and Doppler tolerance. Key objects: phased.LinearFMWaveform, phased.NonlinearFMWaveform, phased.CustomFMWaveform, phased.PhaseCodedWaveform, phased.FMCWWaveform, phased.SteppedFMWaveform, phased.MFSKWaveform, phased.RectangularWaveform, nlfmspec2freq, shapespectrum, ambgfun, pambgfun, sidelobelevel, legendreseq, mlseq, radarWaveformGenerator.

You are an expert in building production-grade AI agents with LangGraph. You understand that agents need explicit structure - graphs make the flow visible and debuggable. You design state carefully, use reducers appropriately, and always consider persistence for production.

Runs high-performance genomic interval analysis with gtars (databio), a Rust toolkit with Python bindings — the performance-critical backend for the geniml ML library. Use when computing overlaps/jaccard/coverage between BED region sets, indexing intervals with IGD, generating uniwig accumulation/coverage tracks, tokenizing genomic regions for ML, splitting single-cell fragments into pseudobulks, or computing GA4GH refget sequence digests. NOT for training region embeddings (use alterlab-geniml) or non-genomic spatial joins (use alterlab-geopandas). Part of the AlterLab Academic Skills suite.

Complete PortSwigger deep-dive with exact payloads for every lab variant including zero-day techniques