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Data Science reference material
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Data Science reference material

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# Data Science Reference Material 



This repostiory is a central location for referencing Data Science, Deep Learning, ML Engineering, and DevOps topics gathered from various blogs, GitHub repos, publications, books, and other various learning material. 



Contributions are certainly welcomed! Please feel free to submit a pull request to recommend any references that should be included in the list below.   

_________________



### Data Science + Machine Learning



- [The Complete Guide to AUC and Average Precision](https://towardsdatascience.com/the-complete-guide-to-auc-and-average-precision-cf1d4647efc3)



- [GridSearchCV 2.0 — New and Improved](https://medium.com/distributed-computing-with-ray/gridsearchcv-2-0-new-and-improved-ee56644cbabf)



- [Explain Your Model with the SHAP Values](https://towardsdatascience.com/explain-your-model-with-the-shap-values-bc36aac4de3d)



- [Houston Area Machine Learning Meetup](https://www.slideshare.net/xuyangela)



- [Machine Learning in Python, SMU](https://github.com/eclarson/MachineLearningNotebooks)



- [Linear Regression with NumPy](https://www.cs.toronto.edu/~frossard/post/linear_regression/)



- [Analyzing Customer Attrition in Subscription Models](https://databricks.com/blog/2020/07/15/analyzing-customer-attrition-in-subscription-models.html)

  

- [Survival Analysis (Lifelines)](https://github.com/CamDavidsonPilon/lifelines)



### Deep Learning 



- [Learning Deep Learning](https://github.com/makci97/learning-deep-learning)



- [Dive into Deep Learning](https://d2l.ai/index.html)

  

- [Stochastic Weight Averaging in PyTorch](https://pytorch.org/blog/stochastic-weight-averaging-in-pytorch/)



- [LSTM is dead. Long Live Transformers!](https://www.youtube.com/watch?v=S27pHKBEp30)



- [PDF Table Extraction with Keras-RetinaNet](https://medium.com/@djajafer/pdf-table-extraction-with-keras-retinanet-173a13371e89)



- [Text Classification with NLP: Tf-Idf vs Word2Vec vs BERT](https://towardsdatascience.com/text-classification-with-nlp-tf-idf-vs-word2vec-vs-bert-41ff868d1794)



- [Deep Learning (Goodfellow-et-al-2016)](http://www.deeplearningbook.org/)



- [Understanding Convolutions](http://colah.github.io/posts/2014-07-Understanding-Convolutions/)



- [Convolutional Neural Networks for Visual Recognition](https://cs231n.github.io/neural-networks-1/)



- [Variational Autoencoders Explained](http://kvfrans.com/variational-autoencoders-explained/)



- [Principles of training multi-layer neural network using backpropagation](http://home.agh.edu.pl/~vlsi/AI/backp_t_en/backprop.html)



- [GloVe: Global Vectors for Word Representation](https://nlp.stanford.edu/projects/glove/)



### Data Wrangling 



- [3 Pandas Trick to Easing Your Data Life](https://towardsdatascience.com/3-pandas-trick-to-easing-your-data-life-984a0dac5311)



- [Do You Know Python Has A Built-In Database?](https://towardsdatascience.com/do-you-know-python-has-a-built-in-database-d553989c87bd)



- [How to rewrite your SQL queries in Python with Pandas](https://towardsdatascience.com/how-to-rewrite-your-sql-queries-in-python-with-pandas-8d5b01ab8e31)



- [Learn Pandas](https://github.com/tdpetrou/Learn-Pandas)



### Time Series



- [Anomaly detection for streaming data using autoencoders](https://github.com/binli826/LSTM-Autoencoders)



- [Intuitive Understanding of the Fourier Transform and FFTs](https://www.youtube.com/watch?v=FjmwwDHT98c)



- [Sktime: a Unified Python Library for Time Series Machine Learning](https://towardsdatascience.com/sktime-a-unified-python-library-for-time-series-machine-learning-3c103c139a55)



### GPUs



- [Rapids.ai (NVIDIA)](https://rapids.ai/)



### CI/CD



- [Travis CI](https://travis-ci.com/)



### MLOps + DataOps



- [Creating End-to-End MLOps pipelines using Azure ML and Azure Pipelines](https://benalexkeen.com/creating-end-to-end-mlops-pipelines-using-azure-ml-and-azure-pipelines-part-1/)



- [MLOps on Azure](https://github.com/microsoft/MLOps)



- [Pachyderm: Data Versioning, Data Pipelines, and Data Lineage](https://github.com/pachyderm/pachyderm)



### Model Management 



- [MLflow Documentation](https://mlflow.org/docs/latest/index.html)



- [Using MLFlow and Docker to Deploy Machine Learning Models](https://medium.com/@paul.bendevis/using-mlflow-and-docker-to-deploy-machine-learning-models-4f7888005e24)



- [Deploy MLflow with docker compose](https://towardsdatascience.com/deploy-mlflow-with-docker-compose-8059f16b6039)



### Visualizations



- [Autoviz: Automatically Visualize any Dataset](https://towardsdatascience.com/autoviz-automatically-visualize-any-dataset-ba2691a8b55a)



- [Voila](https://github.com/voila-dashboards/voila)



### Vim & TMUX



- [Vim Cheatsheet](https://vim.rtorr.com/)



- [Vim Tutorial Videos (TheFrugalComputerGuy)](https://www.youtube.com/watch?v=1xKE62tTYj4&list=PLy7Kah3WzqrEjsuvhT46fr28Q11oa5ZoI&index=25&t=0s)



- [Learn vim For the Last Time: A Tutorial and Primer](https://danielmiessler.com/study/vim/)



- [Resolving merge conflicts with vimdiff)](http://vimcasts.org/episodes/fugitive-vim-resolving-merge-conflicts-with-vimdiff/)



- [Tmux Cheat Sheet & Quick Reference](https://tmuxcheatsheet.com/)