https://github.com/m0nirul/m0nirul.github.io
Own details
https://github.com/m0nirul/m0nirul.github.io
Last synced: 5 months ago
JSON representation
Own details
- Host: GitHub
- URL: https://github.com/m0nirul/m0nirul.github.io
- Owner: m0nirul
- Created: 2019-03-18T08:10:14.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-05-02T09:14:49.000Z (about 7 years ago)
- Last Synced: 2025-07-24T18:43:25.253Z (12 months ago)
- Language: HTML
- Size: 43 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Links From My bookmarkbar dataScience folder(From README.md)
### DataScience
[Andrew Ng: How to Choose Your First AI Project](https://hbr.org/2019/02/how-to-choose-your-first-ai-project?fbclid=IwAR2mK1hfqRmYXRCeWPpIWm7snDt1HOQfHR5hbJINbLlCBcYSmXfn8Yvd3Gc)
[data-science-from-scratch/recommender_systems.py at master · joelgrus/data-science-from-scratch](https://github.com/joelgrus/data-science-from-scratch/blob/master/code-python3/recommender_systems.py)
[tensorFlowZoo](https://github.com/tensorflow/models/tree/master/research/)
[Caffe to TF](https://github.com/ethereon/caffe-tensorflow)
[Caffe Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo)
[W2V](https://www.tensorflow.org/tutorials/representation/word2vec)
[WordEmbedding](http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/)
[ML-DS](http://lineardigressions.com/episodes/2019/3/3/are-machine-learning-engineers-the-new-data-scientists)
MLDeployment
[Systems Perspective to Reproducibility](https://openreview.net/pdf?id=Byl4vavigX)
[Hidden Technical Debt](https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf)
[Bilding Reproducible Pipelie](https://arxiv.org/ftp/arxiv/papers/1810/1810.04570.pdf)
[FB Workflow](https://code.fb.com/ml-applications/introducing-fblearner-flow-facebook-s-ai-backbone/)
[UberEATS prediction](http://proceedings.mlr.press/v67/li17a/li17a.pdf)
[Using Apache Kafka to Drive Cutting-Edge Machine Learning | Confluent](https://www.confluent.io/blog/using-apache-kafka-drive-cutting-edge-machine-learning)
[CODE: kafka-streams-machine-learning-examples: This project contains examples which demonstrate how to deploy analytic models to mission-critical, scalable production environments leveraging Apache Kafka and its Streams API. Models are built with Python, H2O, TensorFlow, Keras, DeepLearning4 and other technologies.](https://github.com/kaiwaehner/kafka-streams-machine-learning-examples)
[Deep Learning With Apache Spark — Part 1 – Towards Data Science](https://towardsdatascience.com/deep-learning-with-apache-spark-part-1-6d397c16abd)
Scikit-learn
[Introduction to scikit-learn - O'Reilly Media](https://www.oreilly.com/ideas/intro-to-scikit-learn)
[Six reasons why I recommend scikit-learn - O'Reilly Media](https://www.oreilly.com/ideas/six-reasons-why-i-recommend-scikit-learn)
[Why you should learn Scikit-learn | Packt Hub](https://hub.packtpub.com/learn-scikit-learn/)
[A Deep Dive Into Sklearn Pipelines | Kaggle](https://www.kaggle.com/baghern/a-deep-dive-into-sklearn-pipelines)
[Sklearn pipelines tutorial | Kaggle](https://www.kaggle.com/sermakarevich/sklearn-pipelines-tutorial)
[Managing Machine Learning Workflows with Scikit-learn Pipelines Part 1: A Gentle Introduction](https://www.kdnuggets.com/2017/12/managing-machine-learning-workflows-scikit-learn-pipelines-part-1.html)
Python Best practices
[A "Best of the Best Practices" (BOBP) guide to developing in Python.](https://gist.github.com/sloria/7001839)
[Python coding standards/best practices - Stack Overflow](https://stackoverflow.com/questions/356161/python-coding-standards-best-practices)
[Python Best Practices – Real Python](https://realpython.com/tutorials/best-practices/)
[Code Style — The Hitchhiker's Guide to Python](https://docs.python-guide.org/writing/style/)
[Pycharm Tutorial](https://www.tutorialspoint.com/pycharm)
[PyCharm - Full Stack Python](https://www.fullstackpython.com/pycharm.html)
[PEP 8 -- Style Guide for Python Code | Python.org](https://www.python.org/dev/peps/pep-0008/)
[github](https://github.com/trainindata/deploying-machine-learning-models)
[featureEng github](https://github.com/solegalli/feature_engine)
[MLWorkshop](https://github.com/sameermahajan/MLWorkshop)
[keras](https://github.com/keras-team/keras)
[lstm anamoly](https://github.com/akash13singh/lstm_anomaly_thesis)
[Anomaly](https://github.com/numenta/NAB)
[Udacity Nanodegree ML](https://github.com/nialloh23/machine-learning-nd)
[udacity OAuth](https://github.com/udacity/OAuth2.0)
Free Books List
[Feature Engineering and Selection: A Practical Approach for Predictive Models](https://bookdown.org/max/FES/)
[1\. Introduction — Dive into Deep Learning documentation](http://d2l.ai/chapter_introduction/intro.html)
[Dive into Deep Learning: An Interactive Book with Math, Code, and Discussions](http://d2l.ai/)
[Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/)
[A Byte of Python](https://python.swaroopch.com/)
[Data Visualization](https://socviz.co/index.html)
[Algorithms of Reinforcement Learning: A new book by Csaba Szepesvari](https://sites.ualberta.ca/~szepesva/RLBook.html)
[web.mit.edu/dimitrib/www/RLbook.html](http://web.mit.edu/dimitrib/www/RLbook.html)
[Think Stats: Probability and Statistics for Programmers](http://www.greenteapress.com/thinkstats/)
[Bayesian Methods for Hackers](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/)
[UnderstandingMachineLearning/](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/)
[The Elements of Statistical Learning](https://web.stanford.edu/~hastie/ElemStatLearn//printings/ESLII_print10.pdf)
[Foundations of Data Science](https://www.cs.cornell.edu/jeh/book.pdf)
[The Ancient Art of the Numerati](http://guidetodatamining.com/)
[Mining of Massive Datasets](http://mmds.org/)
[Deep Learning](http://www.deeplearningbook.org/)
[Machine Learning Yearning](https://www.mlyearning.org/)
[jakevdp/PythonDataScienceHandbook: Python Data Science Handbook: full text in Jupyter Notebooks](https://github.com/jakevdp/PythonDataScienceHandbook)
[Think Bayes – Green Tea Press](http://greenteapress.com/wp/think-bayes/)
[Machine Learning and Big Data](http://www.kareemalkaseer.com/books/ml)
[Statistical Learning with Sparsity: the Lasso and Generalizations](https://web.stanford.edu/~hastie/StatLearnSparsity/)
[Statistical inference for data… by Brian Caffo [PDF/iPad/Kindle]](https://leanpub.com/LittleInferenceBook)
[Convex Optimization – Boyd and Vandenberghe](http://stanford.edu/~boyd/cvxbook/)
[NLTK Book](https://www.nltk.org/book/)
[Automate the Boring Stuff with Python](https://automatetheboringstuff.com/)
[Social Media Mining](http://dmml.asu.edu/smm/)
[Introduction to Machine Learning](http://ai.stanford.edu/~nilsson/mlbook.html)
[UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf)
[Bayesian Reasoning and ML](http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/091117.pdf)
[models/research/textsum at master · tensorflow/models](https://github.com/tensorflow/models/tree/master/research/textsum)
[How to Build a Compelling Data Science Portfolio & Resume | Kaggle - YouTube](https://www.youtube.com/watch?v=xrhPjE7wHas&list=PLqFaTIg4myu-dNobDHQZPrD2wH27PthCG)
[Learn Machine Learning | Data Science | Train In Data](https://www.trainindata.com/learn-machine-learning)