hopefully you already went home and now you may read the DSB! I recommend an interesting article about working with databases with SQLAlchemy from Analytical or an article about limits of gradient descent from Computer Science & Science.
And as always, enjoy your reading.
https://www.patrick-muehlbauer.com/articles/fastapi-with-sqlalchemy – This article is the second part from the series that creates a fullstack application with FastAPI, Nuxt and Postgres. In this part you can learn how to work with databases with help from SQLAlchemy. (rcmd by reader)
https://www.analyticsvidhya.com/blog/2021/08/best-practices-and-performance-tuning-activities-for-pyspark/ – Optimize
your PySpark solution.
https://towardsdatascience.com/25-pandas-functions-you-didnt-know-existed-p-guarantee-0-8-1a05dcaad5d0 – After a long time, a really good list with useful Pandas functions.
Computer Science & Science
https://www.quantamagazine.org/computer-scientists-discover-limits-of-major-research-algorithm-20210817/ – Limits of gradient descent algorithm. What makes it struggle and when.
https://robertheaton.com/bumble-vulnerability/ – Quite detailed, funny and long description of vulnerability in a fictitious dating app allowing to identify the users exact location.
https://sijinjoseph.com/programmer-competency-matrix/ – Find out what your computer science level is in this matrix. Or apply for this online test. (rcmd by reader)
Graphs and Visualizations
https://pair.withgoogle.com/explorables/fill-in-the-blank/ – Multiple interactive graphs showing how BERT can predict the missing words in the sentence. (rcmd by reader)
https://www.andrewheiss.com/blog/2021/08/21/r2-euler/ – I know that all of you are familiar with the coefficient of determination, but this interpretation with help of Venn diagrams is lovely and insightful.
https://octavio.me/posts/2021-07-23-ordinal-viz/ – How to visualize ordinal numbers with help of Bayessians methods.
Business and Career
https://www.bbc.com/news/technology-58331959 – Lol, people investing their money in crypto wild west are surprised that their money can be stolen or lost.
https://cointelegraph.com/news/bitcoin-atm-operators-set-up-association-to-counter-money-laundering – BTM ( Bitcoin ATM) operators are going to require more than just a phone number.
https://www.vice.com/en/article/xgxppk/tmobile-data-breach-class-action-lawsuit – This could be a fatal problem for all sensitive data in the cloud. T-Mobile is facing a lawsuit for its data breach. (rcmd by reader)
https://clario.co/blog/which-company-uses-most-data/ – Which companies have the most sensitive data? (rcmd by reader)
https://www.theverge.com/2021/8/26/22642017/snapchat-scan-feature-ar-camera-visual-search – Snapchat (yeah, still a thing) released Scan. A feature that identifies things in the real world. Interesting part is that it’s heavily powered by a mix of other companies’ recognition engines.
https://www.nytimes.com/2021/08/25/technology/facebook-election-commission.html – FB probably wants to create a commision in order to handle elections objectively.
https://jvns.ca/blog/confusing-explanations/ – Patterns of confusing explanations used by so many authors in so many articles. Very interesting reading.
https://www.pinecone.io/learn/vector-indexes/ – Learn about similarity indexes like Flat, LSH, HNSW, and IVF. When to use which of them.
https://realpython.com/python-deque/ – Queues and stacks are Python basics. Learn how to use them effectively.
Data & Libraries
https://towardsdatascience.com/defining-data-ownership-3fbe95fd0125 – Who’s the owner of the data? Not so simple or is it? (rcmd by reader)
https://www.quantamagazine.org/how-big-data-carried-graph-theory-into-new-dimensions-20210819/ – Thanks to the big data we know that graphs are not enough, it’s time for hypergraphs. A higher-order system brings more ways to model.
https://www.covid-datascience.com/post/israeli-data-how-can-efficacy-vs-severe-disease-be-strong-when-60-of-hospitalized-are-vaccinated – Simpson’s paradox in practice explaining why the stories about vaccine ineffectiveness are nonsense. (rcmd by reader)
https://thegradient.pub/systems-for-machine-learning/ – What is MLOps and what are the challenges of this new field?
https://github.com/visenger/awesome-mlops – Some sources about MLOps. Well, maybe too many for one person, but it’s just a list.
Video & Podcast
https://www.youtube.com/playlist?list=PLwRJQ4m4UJjNymuBM9RdmB3Z9N5-0IlY0 – 6 not so long lectures about Deep RL basics.
https://user2021.r-project.org/recordings/ – Recording from userR! conference.
Papers & Books
https://arxiv.org/abs/2108.07258 – Large fundation models (BERT, GPT-3) are risky since they can contain many hidden flaws and inject them into other models. The authors of the paper bring some solutions.
Behind the Fence
https://angel.co/company/her-1/jobs/336885-senior-data-analyst – Senior Data Analyts in HER, USA.
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