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Over the months I’ve written Tech Tuesdays I’ve received a lot of positive feedback and praise. Thank you all for your thoughtful feedback and comments! This week, I’m reflecting back on some readers’ comments. If you want to reach me directly, please hit the reply button and tell me what’s on your mind. It makes my day.

I’d also like to take this opportunity to make a small ask: if you enjoy reading this newsletter and know a colleague who might also want to read it, please hit the forward button and send it their way. It’s that easy. Thank you!!

Now, some reader comments …

On “The trouble with star ratings”,

KK wrote:
Yes! Even worse are airbnb ratings, where a 4.5/5 stars means something is wrong. Mostly because the vast majority of people who stay in someone’s house, and actually meet and interact with that person, will feel bad to give them anything but 5 stars- even when the place clearly has flaws. And then coffee shops. I want to know how the wifi is and if there are outlets. A shop can have 4.7/5 stars and have non-existent wifi, but everyone was rating the taste of the coffee.

On “Loss and the importance of diversity,” (This was the week I lamented the passing of a dear colleague of mine),

DO wrote:
Life’s ironic juxtaposition between bitter and sweet is never lost on me, and this is certainly a poignant example. Clearly you will keep shining her light through yours, and for that I am thankful, and we are all the better for it. Take care my friend – keep on the good fight, but most importantly, keep on the great dance!!

BK wrote:
I’m very sorry about the loss of your close friend Zank. It’s heartbreaking to hear stories like this. It’s my 14th wedding anniversary today and I have three young healthy boys and a loving wife. Life can get crazy at times, and stories like this make you realize just how fleeting life can be and a reminder of what matters most. May her memory be eternal.

ES wrote:
Thank you for sharing Liz’s story and her tribute. It’s really sad to see someone with such great influence and potential cut short. This is a great post, we too often forget to thank and express our gratitude towards those that positively influence us, I thank you for that.

On “First hires when building a data science team”,

LP wrote:
Lots of good lessons here for building a first team. Might want to follow up with how to find these folks e.g. look for experienced people who’ve worked with data but may not have had the data scientist title.

We’re putting all our TechTuesday newsletters on their own Bennett Data Science webpage soon. If you want to go back and find something or share with your colleagues, we’ll soon have you covered. We’ll let you know when it’s live.

Of Interest

State-of-the-Art Natural Language Processing in Ten Lines
Here’s a natural language processing (NLP) library that’s getting a lot of attention and uses some of the most current and powerful NLP models to date.

Supervised vs. Unsupervised Learning
Supervised and unsupervised learning are two of the three major branches of machine learning (the other is reinforcement learning), but what’s the difference? Why do we need both? Which one is better? Let’s get into it!——data_science-5

Predicting Startup Failures Using Classification
Using data from CrunchBase, this data scientist looks at how to predict startup success or failure. The more money a company raises the more likely it is to succeed. And, for each additional $1 Million a company raises per round (on average) their odds of success will increase by 16%. The longer a company can last between funding rounds, the better it is for them; each additional month between funding rounds increases the odds of success by 5%. Read more here:——data_science-5

Language Processing That’s So Good Humans Can’t Tell It’s a Machine
Remember that scary AI text-generator that was too dangerous to release? It’s out now: