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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Notes about various data science, machine-learning, deep-learning APIs \n\n\n## Notebooks\n\n\nReference, Guides, Tutorials:\n\n* [TensorFlow Extended (TFX)](/tfx)\n* [TFRecord, tf.train.Example](/tf_record_tftrain)\n* [tf.Transform API](/tf_transform)\n* [TensorFlow Serving](/tf_server)\n* [TensorFlow Probability](tf_probability)\n\n\nBook Material:\n\n* [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow](/book_hands_on)\n\t- Excellent foundation for both Scikit-Learn and Deep Learning, probably the best single resource there is.\n* [Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow](/book_building_ml_pipelines)\n\t- Good introduction to TFX, though a bit behind the latest API versions.\n* [Bayesian Methods for Hackers](/book_probablistic_programming)\n\t- Great introduction to probabilistic programming, especially the Bayesian approach.\n\n---\n\n## Useful papers\n\n\n[Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://arxiv.org/abs/2205.02302)\n\n[Challenges in Deploying Machine Learning: a Survey of Case Studies, v3 (2022)](https://arxiv.org/abs/2011.09926)\n\n[TFX: A TensorFlow-Based Production-Scale Machine Learning Platform (2017)](https://research.google/pubs/pub46484/)\n\n[Data Validation for Machine Learning (2019)](https://research.google/pubs/pub47967/)\n\n[Hidden Technical Debt in Machine Learning Systems (2015)](https://proceedings.neurips.cc/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html)\n\n[Hidden technical debt in machine learning systems (日本語資料)](https://www.slideshare.net/Gushi/hidden-technical-debt-in-machine-learning-systems)\n\n[Machine Learning: The High Interest Credit Card of Technical Debt (2014)](https://research.google/pubs/pub43146/)\n\n[AutoGraph: Imperative-style Coding with Graph-based Performance (2019)](https://research.google/pubs/pub47990/)\n\n[TensorFlow Data Validation: Data Analysis and Validation in Continuous ML Pipelines (2020)](https://dl.acm.org/doi/abs/10.1145/3318464.3384707)\n\n[Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX) (2021)](https://arxiv.org/abs/2010.02013)\n\n[tf.data: A Machine Learning Data Processing Framework (2021)](https://arxiv.org/abs/2101.12127)\n\n[TensorFlow: A system for large-scale machine learning (2016)](https://arxiv.org/abs/1605.08695)\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgbih%2Fmachine_learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgbih%2Fmachine_learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgbih%2Fmachine_learning/lists"}