Cherie M. Poland MA, MS, JD

@PolandCherieM

Mom. Polymath. Machine Learning Engineer. Problem Solver. Candidate: Masters of Engineering (CSA) @ Virginia Tech (NCR). Opinions are my own.

Northern Virginia (DC metro)
Vrijeme pridruživanja: lipanj 2019.

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  1. Prikvačeni tweet

    Please excuse the typos. They are products of my speed-typing, dyslexia, auto-correct & the lack of an edit function on here. Chaos is easy. Order is hard. Yet, one cannot exist without the other. Eternally & immortally bound, one to the other.

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  2. An allegory: Turn up the heat of water too fast & the frog jumps out of the pot. Turn up the heat just enough & it sweats, squirms & reacts. Interesting when you think about the otherwise subtle transitions of policy matters. Sometimes the “too obvious” is a shock to the system.

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  3. The best comment to the video of Nancy Pelosi’s speech tearing that I’ve run across is by who titled the video: “Reviewer #3.” 1. Love Science Twitter! 2. We all know that crazy peer review happens. 3. Humor is the true slayer of darkness. 4. Occam’s Razor.

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  4. Tearing up the copy of the speech showed an absolute lack of decorum on the part of the Speaker of the House. Americans deserve better.

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  5. Had to rewind that moment on DVR & do a double-take to make sure it wasn’t a deepfake. WOW, just WOW! Hope that was Pelosi’s personal copy & not an official government document. Trigger warning: inflammatory remark/rhetorical Q: Isn’t she doing the same thing to the Constitution?

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  6. Loved the SOTU speech. Can’t fit all of the accolades in a Tweet, but it was impressive and moving. Great job!

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  7. Sad to hear that Rush Limbaugh has lung cancer (as I would be for anyone w similar diagnosis), but he loved cigars. Wish he & his family a speedy recovery. Have disagreed w him over the years, but he is also often correct. Smoking changes things biochemically. Smoking kills! EIB

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  8. Love it! N-gram models in computational linguistics are compared to k-mers in computational biology for polymers (e.g. oligomers). Both use Markov model processing. Home! :D

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  9. Its Zootopia out there. Go have a great day & week.

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  10. Reading a biography of Oliver Heaviside. What an amazing human being. Self-taught. Was an early telegrapher & understood EM from the lines up. Bucked the establishment to explain the math in new ways. Is largely responsible for EE as we know it, including the math of JCM’s eqns.

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  11. Power of suggestion: saw a cremini risotto pic in a post. Found a small bag of Arborio rice in the pantry and an hour later was enjoying my own “scrounge the pantry” version of baby portobello and vegan smoked Gouda risotto. Reason number 9397 to keep a well-stocked pantry.

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  12. Context + inference continues with concentric complexities. As Shrek says, “its complicated” like onions with layers. Dealing w/ new sets of acronyms in NLP depending on packages = differences in parts of speech, parsing, & label parsing (not even actual text) adds to complexity.

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  13. 2/2 immunosuppression. If blood tests on coronavirus patients reveal leukopenia & mild thrombocytopenia. Where are those cells going if there is no internal bleed? Someone run a CBC on bronchial lavage & publish results, please!

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  14. 1/2 Thinking about the leukopenia in the unapproved drug compassionate use case. Dawned on me that it could be a site-of measurement issue. Blood leukopenia b/c of WBCs driven to lungs due to coronavirus. In SARS, steroids worked. Immunosuppressive & anti-inflammatory b/c of

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  16. 70,000 GPU hours just boggles my mind. No doubt that is an accurate number. Sounds right based on experience. Just wow, though.

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  17. Will have to play with this. Git repo is in link.

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  18. The guy sitting behind me also works in ML/AI and has a MS in CS. 3 of us sitting next to each other on a random train is ... a good sign.

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  19. The guy across the aisle from me on the train is reading O'Reilly's Hands-On ML with Sci-kit learn, Keras, and Tensorflow. Random ... and not. Democratization. Or intersting neighborhood?

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  20. Concise article. Extrapolation problem, especially with time series data.

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  21. Never realized that Veronica Roth found a model for Tris (Beatrice) in Marcus Aurelius Antonious. The parallels are uncanny.

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