{"id":29746686,"url":"https://github.com/m0nirul/m0nirul.github.io","last_synced_at":"2026-02-05T09:40:08.115Z","repository":{"id":306268126,"uuid":"176233789","full_name":"m0nirul/m0nirul.github.io","owner":"m0nirul","description":"Own 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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-07-26T07:43:28.839Z","updated_at":"2026-02-05T09:40:08.098Z","avatar_url":"https://github.com/m0nirul.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Links From My bookmarkbar dataScience folder(From README.md)\n\n### DataScience\n\n\n\n[Andrew Ng: How to Choose Your First AI Project](https://hbr.org/2019/02/how-to-choose-your-first-ai-project?fbclid=IwAR2mK1hfqRmYXRCeWPpIWm7snDt1HOQfHR5hbJINbLlCBcYSmXfn8Yvd3Gc)\n\n[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)\n\n[tensorFlowZoo](https://github.com/tensorflow/models/tree/master/research/)\n\n[Caffe to TF](https://github.com/ethereon/caffe-tensorflow)\n\n[Caffe Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo)\n\n[W2V](https://www.tensorflow.org/tutorials/representation/word2vec)\n\n[WordEmbedding](http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/)\n\n[ML-DS](http://lineardigressions.com/episodes/2019/3/3/are-machine-learning-engineers-the-new-data-scientists)\n\n\n\n\u003cdetails\u003e\n  \u003csummary\u003eMLDeployment\u003c/summary\u003e\n\n\n\n\n\n[Systems Perspective to Reproducibility](https://openreview.net/pdf?id=Byl4vavigX)\n\n[Hidden Technical Debt](https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf)\n\n[Bilding Reproducible Pipelie](https://arxiv.org/ftp/arxiv/papers/1810/1810.04570.pdf)\n\n[FB Workflow](https://code.fb.com/ml-applications/introducing-fblearner-flow-facebook-s-ai-backbone/)\n\n[UberEATS prediction](http://proceedings.mlr.press/v67/li17a/li17a.pdf)\n\n[Using Apache Kafka to Drive Cutting-Edge Machine Learning | Confluent](https://www.confluent.io/blog/using-apache-kafka-drive-cutting-edge-machine-learning)\n\n[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)\n\n[Deep Learning With Apache Spark — Part 1 – Towards Data Science](https://towardsdatascience.com/deep-learning-with-apache-spark-part-1-6d397c16abd)\n\u003c/details\u003e\n\n\n\n\u003cdetails\u003e\n  \u003csummary\u003eScikit-learn\u003c/summary\u003e\n\n[Introduction to scikit-learn - O'Reilly Media](https://www.oreilly.com/ideas/intro-to-scikit-learn)\n\n[Six reasons why I recommend scikit-learn - O'Reilly Media](https://www.oreilly.com/ideas/six-reasons-why-i-recommend-scikit-learn)\n\n[Why you should learn Scikit-learn | Packt Hub](https://hub.packtpub.com/learn-scikit-learn/)\n\n[A Deep Dive Into Sklearn Pipelines | Kaggle](https://www.kaggle.com/baghern/a-deep-dive-into-sklearn-pipelines)\n\n[Sklearn pipelines tutorial | Kaggle](https://www.kaggle.com/sermakarevich/sklearn-pipelines-tutorial)\n\n[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)\n\n\u003c/details\u003e\n\n\n\n\u003cdetails\u003e\n  \u003csummary\u003ePython Best practices\u003c/summary\u003e\n\n\n\n[A \"Best of the Best Practices\" (BOBP) guide to developing in Python.](https://gist.github.com/sloria/7001839)\n\n[Python coding standards/best practices - Stack Overflow](https://stackoverflow.com/questions/356161/python-coding-standards-best-practices)\n\n[Python Best Practices – Real Python](https://realpython.com/tutorials/best-practices/)\n\n[Code Style — The Hitchhiker's Guide to Python](https://docs.python-guide.org/writing/style/)\n\n[Pycharm Tutorial](https://www.tutorialspoint.com/pycharm)\n\n[PyCharm - Full Stack Python](https://www.fullstackpython.com/pycharm.html)\n\n[PEP 8 -- Style Guide for Python Code | Python.org](https://www.python.org/dev/peps/pep-0008/)\n\n\n\n\n\n[github](https://github.com/trainindata/deploying-machine-learning-models)\n\n[featureEng github](https://github.com/solegalli/feature_engine)\n\n[MLWorkshop](https://github.com/sameermahajan/MLWorkshop)\n\n[keras](https://github.com/keras-team/keras)\n\n[lstm anamoly](https://github.com/akash13singh/lstm_anomaly_thesis)\n\n[Anomaly](https://github.com/numenta/NAB)\n\n[Udacity Nanodegree ML](https://github.com/nialloh23/machine-learning-nd)\n\n[udacity OAuth](https://github.com/udacity/OAuth2.0)\n\n\n\n\n\n\n\n\u003c/details\u003e\n\n\n\n\u003cdetails\u003e\n  \u003csummary\u003eFree Books List\u003c/summary\u003e\n\n\n\n[Feature Engineering and Selection: A Practical Approach for Predictive Models](https://bookdown.org/max/FES/)\n\n[1\\. Introduction — Dive into Deep Learning documentation](http://d2l.ai/chapter_introduction/intro.html)\n\n[Dive into Deep Learning: An Interactive Book with Math, Code, and Discussions](http://d2l.ai/)\n\n[Interpretable Machine Learning](https://christophm.github.io/interpretable-ml-book/)\n\n[A Byte of Python](https://python.swaroopch.com/)\n\n[Data Visualization](https://socviz.co/index.html)\n\n[Algorithms of Reinforcement Learning: A new book by Csaba Szepesvari](https://sites.ualberta.ca/~szepesva/RLBook.html)\n\n[web.mit.edu/dimitrib/www/RLbook.html](http://web.mit.edu/dimitrib/www/RLbook.html)\n\n[Think Stats: Probability and Statistics for Programmers](http://www.greenteapress.com/thinkstats/)\n\n[Bayesian Methods for Hackers](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/)\n\n[UnderstandingMachineLearning/](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/)\n\n[The Elements of Statistical Learning](https://web.stanford.edu/~hastie/ElemStatLearn//printings/ESLII_print10.pdf)\n\n[Foundations of Data Science](https://www.cs.cornell.edu/jeh/book.pdf)\n\n[The Ancient Art of the Numerati](http://guidetodatamining.com/)\n\n[Mining of Massive Datasets](http://mmds.org/)\n\n[Deep Learning](http://www.deeplearningbook.org/)\n\n[Machine Learning Yearning](https://www.mlyearning.org/)\n\n[jakevdp/PythonDataScienceHandbook: Python Data Science Handbook: full text in Jupyter Notebooks](https://github.com/jakevdp/PythonDataScienceHandbook)\n\n[Think Bayes – Green Tea Press](http://greenteapress.com/wp/think-bayes/)\n\n[Machine Learning and Big Data](http://www.kareemalkaseer.com/books/ml)\n\n[Statistical Learning with Sparsity: the Lasso and Generalizations](https://web.stanford.edu/~hastie/StatLearnSparsity/)\n\n[Statistical inference for data… by Brian Caffo [PDF/iPad/Kindle]](https://leanpub.com/LittleInferenceBook)\n\n[Convex Optimization – Boyd and Vandenberghe](http://stanford.edu/~boyd/cvxbook/)\n\n[NLTK Book](https://www.nltk.org/book/)\n\n[Automate the Boring Stuff with Python](https://automatetheboringstuff.com/)\n\n[Social Media Mining](http://dmml.asu.edu/smm/)\n\n[Introduction to Machine Learning](http://ai.stanford.edu/~nilsson/mlbook.html)\n\n[UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf](http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf)\n\n[Bayesian Reasoning and ML](http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/091117.pdf)\n\u003c/details\u003e\n\n\n\n\n\n[models/research/textsum at master · tensorflow/models](https://github.com/tensorflow/models/tree/master/research/textsum)\n\n[How to Build a Compelling Data Science Portfolio \u0026 Resume | Kaggle - YouTube](https://www.youtube.com/watch?v=xrhPjE7wHas\u0026list=PLqFaTIg4myu-dNobDHQZPrD2wH27PthCG)\n\n[Learn Machine Learning | Data Science | Train In Data](https://www.trainindata.com/learn-machine-learning)\n\n\n\n\n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fm0nirul%2Fm0nirul.github.io","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fm0nirul%2Fm0nirul.github.io","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fm0nirul%2Fm0nirul.github.io/lists"}