Results for “data-preprocessing”

Reduce high-dimensional data with UMAP for visualization, clustering preprocessing, and supervised or semi-supervised learning, including parameter tuning guidance.

Train, evaluate, export, and run inference for Masked Auto-Encoder (MAE) models for self-supervised pretraining and fine-tuning of visual representations.

Scans a DICOM series folder to extract header metadata and produce a preflight verdict without decoding pixel data.

Designs and implements big data architectures, processes large-scale datasets with distributed systems, and optimizes data pipelines for throughput using Hadoop, Spark, and cloud platforms.

Data analysis workflow from import through modeling and communication. Use when analyzing a dataset, exploring data, building a statistical model, selecting features, or communicating findings to stakeholders.

Kinetics-400: A Large Video Understanding Dataset

Turn raw ideas into one clear pre-planning concept artifact by choosing the right framing mode: problem framing, audience and value framing, concept shaping, game concept framing, or story packaging. Use when the user needs the next artifact before PRDs, sprint plans, launch execution, or game-production routing.

Process large-scale ML datasets with distributed streaming execution across CPU/GPU, supporting Parquet, CSV, JSON, images, and integration with PyTorch, TensorFlow, and Ray Train.

Execute and analyze AI Data Poisoning attacks. By subtly injecting malicious or targeted misinformation into an LLM's training or fine-tuning dataset, an attacker can covertly manipulate the model's future outputs, implant backdoors, or enforce biases without altering the model architecture.

Guides analysis of Neuropixels recordings from raw data to curated units, covering preprocessing, motion correction, spike sorting, quality metrics, and export.

Profile an unfamiliar dataset — shape, grain, quality, nulls, distributions, and duplicates — before any analysis is trusted.

SBU Captions Dataset

Builds a configurable scraping agent that collects data from APIs, HTML, or RSS, enriches it with Gemini AI scoring, and stores results in Notion, Google Sheets, Supabase, or local files.

Train machine learning models end-to-end, covering data loading, preprocessing, architecture selection, training loops, validation, and checkpointing. Use when the user requests model training or provides relevant inputs for this workflow.

Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.

Extract and preprocess tiles from whole-slide images (WSI) with histolab — OpenSlide-backed slide loading, tissue detection and masks, Random/Grid/Score tile extraction, and image/morphological filters for H&E preprocessing. Use when the user needs lightweight WSI slide preprocessing — building tile datasets for ML training, tissue segmentation, or quick tile-based inspection of histopathology slides. For end-to-end computational-pathology, deep-learning model training, nucleus segmentation, or multiplexed/spatial-proteomics (CODEX, Vectra) pipelines prefer alterlab-pathml instead. Part of the AlterLab Academic Skills suite.

Run full computational-pathology workflows with PathML — whole-slide-image (WSI) analysis across 160+ slide formats, multiplexed immunofluorescence (CODEX, Vectra, MERFISH), nucleus segmentation/classification (HoVer-Net, HACTNet), tissue- and cell-graph construction, HDF5 dataset management, and deep-learning model training on pathology data. Use when the user builds end-to-end deep-learning pathology pipelines, analyzes multiplexed or spatial-proteomics slides, or segments nuclei. For lightweight H&E slide preprocessing, tissue masking, or plain Random/Grid/Score tile extraction prefer alterlab-histolab instead. Part of the AlterLab Academic Skills suite.