Deep Learning Reference

Wed, Jan 10, 2018

Read in 1 minutes

Something I use for starting

  1. https://towardsdatascience.com/linear-algebra-cheat-sheet-for-deep-learning-cd67aba4526c (Linear Algebra basic)
    1. https://harishnarayanan.org/writing/artistic-style-transfer/
    2. https://appliedgo.net/perceptron/#inside-an-artificial-neuron
    3. https://www.khanacademy.org/math/multivariable-calculus/multivariable-derivatives/partial-derivative-and-gradient-articles/a/introduction-to-partial-derivatives
    4. https://medium.com/@nokkk/jupyter-notebook-tricks-for-data-science-that-enhance-your-efficiency-95f98d3adee4
    5. https://medium.com/@bwest87/building-a-deep-neural-net-in-google-sheets-49cdaf466da0
    6. https://codeburst.io/jupyter-notebook-tricks-for-data-science-that-enhance-your-efficiency-95f98d3adee4
    7. https://najeebkhan.github.io/blog/VecCal.html (Jacobian vs Hessian)
    8. Optimization algo bird eye view http://fa.bianp.net/teaching/2018/eecs227at/
  2. https://nbviewer.jupyter.org/github/groverpr/learn_python_libraries/blob/master/pandas/pandas_cheatsheet.ipynb basic pandas df
  3. https://github.com/Stephen-Rimac/Python-for-Data-Scientists/blob/master/Python%20for%20Data%20Scientists.ipynb data scientist
  4. https://towardsdatascience.com/beyond-accuracy-precision-and-recall-3da06bea9f6c precision and recall
  5. https://medium.com/@yu4u/why-mobilenet-and-its-variants-e-g-shufflenet-are-fast-1c7048b9618d mobilenet
  6. https://qiita.com/odanado/items/ffb685ba48f8a2a51683 embedding visualization
  7. https://medium.com/@shivamgoel1791/everything-you-need-to-know-about-neural-style-transfer-994530cc9a6e neural style transfer
  8. https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/ // nmt with attention
  9. https://www.kaggle.com/annavictoria/ml-friendly-public-datasets
  10. https://machinelearningmastery.com/how-to-use-statistics-to-identify-outliers-in-data/?__s=soesfvn8qaszfihuwqqp
  11. https://tuatini.me/part-1-how-to-setup-your-own-environment-for-deep-learning/
  12. https://christophm.github.io/interpretable-ml-book/intro.html
  13. https://towardsdatascience.com/semantic-segmentation-with-deep-learning-a-guide-and-code-e52fc8958823

Visualization

  1. https://projector.tensorflow.org/
  2. https://github.com/tensorflow/lucid#notebooks
  3. https://medium.com/@Zelros/a-brief-history-of-machine-learning-models-explainability-f1c3301be9dc
  4. http://www.benfrederickson.com/numerical-optimization/