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Understanding UMAP - Google PAIR
pair-code.github.io /understanding-umap/
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MIT OpenCourseWare: Introduction To Machine Learning
ocw.mit.edu /courses/6-036-introduction-to-machine-learning-fall-2020/
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Style Guide for Python Code: PEP 8
peps.python.org /pep-0008/
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MIT OpenCourseWare: Statistical Learning Theory
ocw.mit.edu /courses/18-465-topics-in-statistics-statistical-learning-theory-spring-2007/
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MIT OpenCourseWare: Mathematics Of Machine Learning
ocw.mit.edu /courses/18-657-mathematics-of-machine-learning-fall-2015/
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Durham University Materials for COMP3547 (Deep Learning) and COMP3667 (Reinforcement Learning) from Dr. Robert Lieck
github.com /cwkx/materials/tree/main
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Rules of Machine Learning from Google
developers.google.com /machine-learning/guides/rules-of-ml
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Coding Practices for Python/ML
github.com /zedr/clean-code-python
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Tutorial: Image Recognition with CNN in Matlab
hevpdd.ca /publications-and-case-studies/case-study-image-recognition-with-convolutional-neural-networks/
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Tutorial: State of Charge Estimation with EKF and SVSF in Matlab
hevpdd.ca /case-study-state-of-charge-estimation-2/
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Standford University Cheat Sheets for ML (web version)
stanford.edu /~shervine/teaching/
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Materials from CORNELL CS4780/CS5780: Machine Learning for Intelligent Systems
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Good collection of introductions to topics for stats and machine learning: Nature Methods' Points of Significance
www.nature.com /collections/qghhqm/pointsofsignificance
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Deep learning: the unintuitive relationship between overparameterization, overfitting and generalization
brain.harvard.edu /hbi_news/how-neural-networks-escape-perils-of-overparameterization/
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What's the kernel trick? (explanation)
eranraviv.com /what-is-the-kernel-trick/
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A good introduction/overview to neural network theory: Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville (Free, online copy)
www.deeplearningbook.org