software stuffs and open-source projects I help develop

• TweetyNet
  https://github.com/yardencsGitHub/tweetynet
  Hybrid convolutional-recurrent neural network for segmentation of birdsong and classification of elements
  in collaboration with Yarden Cohen

• vak
  https://github.com/NickleDave/vak
  library to work with neural networks that segment and annotate vocalizations

• Crowsetta
  https://github.com/NickleDave/crowsetta
  a tool to work with any format for annotating birdsong

• hybrid-vocal-classifier: voice-to-text for songbirds
  https://hybrid-vocal-classifier.readthedocs.io/en/latest/
  https://github.com/NickleDave/hybrid-vocal-classifier
  A library for comparing supervised learning algorithms that automate labeling the elements of birdsong

• evfuncs
  https://github.com/NickleDave/evfuncs
  Functions for working with files created by the EvTAF program and the evsonganaly GUI.
  The functions can be used to work with Bengalese finch song in this data repository:
  https://figshare.com/articles/Bengalese_Finch_song_repository/4805749

• koumura
  https://github.com/NickleDave/koumura
  Functions for working with this data repository:
  https://figshare.com/articles/BirdsongRecognition/3470165

• ML-comparison-birdsong
  https://github.com/NickleDave/ML-comparison-birdsong
  Code to accompany the SciPy 2016 Proceedings Paper "Comparison of machine learning methods applied to birdsong element classification"
  http://conference.scipy.org/proceedings/scipy2016/david_nicholson.html

• songdkl
  https://github.com/NickleDave/songdkl
  a measure of similarity between birdsongs (and other things)
  As described in:
  Mets, David G., and Michael S. Brainard. "An automated approach to the quantitation of vocalizations and vocal learning in the songbird." PLoS computational biology 14.8 (2018): e1006437.
  https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006437&rev=2
  in collaboration with David Mets

• visual-search-nets
  https://github.com/NickleDave/visual-search-nets
  Experiments to measure the behavior of convolutional neural networks performing a visual search task.

• searchstims
  https://github.com/NickleDave/searchstims
  Python package to make stimuli like those used in classic visual search experiments.

• Recurrent Models of Visual Attention
  https://github.com/NickleDave/thrillington
  Replication of "Recurrent models of visual attention", Mnih et al. 2014

• aver: active vision models in Nengo
  https://github.com/NickleDave/aver
  framework for visual search models that incorporate eye movements.
  proposal for Nengo summer school 2019.

• learning-curves
  https://github.com/NickleDave/learning-curves
  Study of Cortes et al. 1994 "Learning Curves" paper with scikit-learn and scipy for first Atlanta Jupyter Day.

• Data Science for Scientists ATL
  https://data-science-for-scientists-atl.github.io/
  https://github.com/Data-Science-for-Scientists-ATL

• BEST data science short course
  https://github.com/NickleDave/BEST-data-science-short-course
  Short course in scientific computing and data science sponsored by the Atlanta BEST program.

• EWIN Coding Bootcamp
  https://github.com/NickleDave/EWIN-coding-bootcamp
  Coding bootcamp sponsored by Emory Women In Neuroscience