adiezsanchez

adiezsanchez's Stars

• poloclub/cnn-explainer

  Learning Convolutional Neural Networks with Interactive Visualization.

  Language:JavaScript8.2k165481.2k

• SkalskiP/courses

  This repository is a curated collection of links to various courses and resources about Artificial Intelligence (AI)

  Language:Python5.4k917491

• NirDiamant/GenAI_Agents

  This repository provides tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive AI systems.

  Language:Jupyter Notebook4.3k572509

• bnsreenu/python_for_microscopists

  https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1

  Language:Jupyter Notebook4k109752.4k

• AssemblyAI-Community/ML-Study-Guide

  Minimal Machine Learning Study Plan

  1.9k492384

• royerlab/napari-chatgpt

  A napari plugin to process and analyse images with chatGPT!

  Language:CSS250112726

• scottleibrand/gpt-summarizer

  Extract text from PDF, summarize each section w/ GPT, and provide a summarized outline of the paper

  Language:Python1877927

• haesleinhuepf/stackview

  Interactive image stack viewing in jupyter notebooks based on ipycanvas and ipywidgets

  Language:Python13783510

• BiaPyX/BiaPy

  Open source Python library for building bioimage analysis pipelines

  Language:Jupyter Notebook12654528

• HenriquesLab/DL4MicEverywhere

  Bringing the ZeroCostDL4Mic experience running everywhere via easy-to-install docker images

  Language:Jupyter Notebook7061910

• weigertlab/spotiflow

  Accurate and efficient spot detection for microscopy data

  Language:Python623147

• haesleinhuepf/napari-accelerated-pixel-and-object-classification

  GPU-accelerated, OpenCL-based Random Forest Classifiers for pixel and labeled object classification in napari.

  Language:Python435177

• BiAPoL/PoL-BioImage-Analysis-TS-GPU-Accelerated-Image-Analysis

  PoL Bioimage Analysis Training School: GPU-Accelerated Image Analysis Track

  Language:Cool251189

• imagej/napari-imagej

  Use ImageJ functionality from napari

  Language:Python2561744

• haesleinhuepf/napari-segment-blobs-and-things-with-membranes

  A plugin based on scikit-image for segmenting nuclei and cells based on fluorescent microscopy images with high intensity in nuclei and/or membranes.

  Language:Python232228

• BiAPoL/imagej-macro-cheat-sheet

  19742

• dlmbl/DL-MBL-2024

  Language:Python16412

• zeissmicroscopy/ZEN_Python_OAD_workshop

  Material to showcase and test several tools and packages releated to ZEN, CZI image files and python packages related to those topics

  Language:Jupyter Notebook15401

• ved-sharma/HyperStackReg

  An ImageJ/FIJI plugin for multi-channel Hyperstack alignment (registration)

  Language:Java11111

• sebi06/czitools

  This repository provides a collection of tools to simplify reading CZI (Carl Zeiss Image) pixel and metadata in Python. In addition it also contains other useful utilities to visualize CZI images inside Napari.

  Language:Python8284

• CamachoDejay/Teaching-ImageJ-FIJI

  Language:ImageJ Macro5400

• True-North-Intelligent-Algorithms/deconvolution-gpu-dl-course

  Course: Exploring the Intersection of Deconvolution, GPU Computing, and Deep Learning

  Language:Shell5101

• BiAPoL/on_the_fly_image_processing_napari

  Language:Jupyter Notebook4412

• bioimage-io/AdvanceImageAnalysisEMBO2023

  4601

• CellMigrationLab/Plot-Stats

  Jupiter notebooks created to help us plot and analyse our datasets

  Language:Jupyter Notebook4010

• ctr26/pytorch_tutorial

  pyTorch demo(s)

  Language:Jupyter Notebook4212

• dlegland/image-analysis-practical-2023

  Language:ImageJ Macro4101

• ome/EMBL-EBI-imaging-course-04-2024

  Language:Jupyter Notebook4762

• LSeu-Open/awesome-biological-image-analysis

  A curated list of softwares, tools, pipelines, plugins etc. for image analysis related to biological questions.

  3001

• Laboratory-Imaging/JOBS-examples

  Examples of JOBS recipes. JOBS is a smart image acquisition engine in NIS-Elements.

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