cookbook/.todo at master · tomis9/cookbook · GitHub

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<?xml version="1.0"?>
<todo version="0.1.20">
    <note priority="medium" time="1546704855">
        programming
        <note priority="medium" time="1546704926">
            clean code python
        </note>
        <note priority="medium" time="1546704941">
            class inheritance python
        </note>
        <note priority="medium" time="1546704950">
            parallel R https://www.r-bloggers.com/how-to-go-parallel-in-r-basics-tips/
        </note>
        <note priority="medium" time="1546704955">
            singletons in Python https://stackoverflow.com/questions/6760685/creating-a-singleton-in-python
        </note>
        <note priority="medium" time="1547671684">
            python design patterns
        </note>
        <note priority="medium" time="1547671755">
            REST api in flask https://blog.miguelgrinberg.com/post/designing-a-restful-api-with-python-and-flask
        </note>
        <note priority="medium" time="1551720127">
            http://www.davekleinschmidt.com/r-packages/
        </note>
    </note>
    <note priority="medium" time="1546704869">
        data engineering
        <note priority="medium" time="1546704988">
            kafka
        </note>
        <note priority="medium" time="1546705000">
            GCP, AWS, Azure
        </note>
        <note priority="medium" time="1546705008">
            Hive
        </note>
        <note priority="medium" time="1546705022">
            Sqoop
        </note>
        <note priority="medium" time="1546705028">
            graph databases
        </note>
        <note priority="medium" time="1548097216">
            h2o + rsparkling
        </note>
        <note priority="medium" time="1549291351">
            feather https://github.com/wesm/feather
        </note>
    </note>
    <note priority="medium" time="1546705047">
        machine learning
        <note priority="medium" time="1546705053">
            deep learning
        </note>
        <note priority="medium" time="1546705056">
            keras
        </note>
        <note priority="medium" time="1546705060">
            tensorflow
            <note priority="medium" time="1548626859">
                song popularity https://towardsdatascience.com/how-i-created-a-classifier-to-determine-the-potential-popularity-of-a-song-6d63093ba221
            </note>
        </note>
        <note priority="medium" time="1546705069">
            Doing Bayesian Data Analysis
        </note>
        <note priority="medium" time="1546705073">
            Pytorch
            <note priority="medium" time="1548611390">
                pytext https://code.fb.com/ai-research/pytext-open-source-nlp-framework/
            </note>
            <note priority="medium" time="1548611466">
                pytext https://towardsdatascience.com/introducing-pytext-d8f404f1745
            </note>
        </note>
        <note priority="medium" time="1546705077">
            Theano
        </note>
        <note priority="medium" time="1546705080">
            computer vision
        </note>
        <note priority="medium" time="1546800955">
            caffee
        </note>
        <note priority="medium" time="1547671764">
            MCMC
        </note>
        <note priority="medium" time="1547671772">
            metropolis algorithm
        </note>
        <note priority="medium" time="1548320923">
            xgboost https://medium.com/@chrisfotache/text-classification-in-python-pipelines-nlp-nltk-tf-idf-xgboost-and-more-b83451a327e0
        </note>
        <note priority="medium" time="1548322027">
            naive bayes
            <note priority="medium" time="1548626961">
                sentiment analysis + naive bayes explanation https://www.datacamp.com/community/tutorials/simplifying-sentiment-analysis-python
            </note>
        </note>
        <note priority="medium" time="1548322692">
            falling rule lists
        </note>
        <note priority="medium" time="1548323704">
            https://github.com/facebookresearch/LASER
        </note>
        <note priority="medium" time="1548621888">
            Eperience in trend analyses, multivariate statistics (parametric / non-parametric), sampling, bias reduction, indirect estimation, data aggregation techniques, automation, generalization
        </note>
        <note priority="medium" time="1549898940">
            softmax https://medium.com/data-science-bootcamp/understand-the-softmax-function-in-minutes-f3a59641e86d
        </note>
        <note priority="medium" time="1550827924">
            RNN and CNN
        </note>
    </note>
    <note priority="medium" time="1551720104">
        g -1
    </note>
</todo>