For many people reading this article, brutalist web design or even brutalism itself might be a new discovery, in which c

There is a scene where we see Beth’s father has remarried and had another child — her half sibling. This moment hit hard for me. I’ve spent a whole life separated from 6 siblings I never knew I had until recently. One of them died 8 years ago, so I will never get a chance to know him. Perhaps it was ‘for the best’ that I was not raised by my biological parents, but that does not make it right that I was never allowed to know my brothers and sisters, or that our children — first cousins — might never know each other if not for DNA testing and my own obsessiveness. Likewise, there are aunts, uncles, cousins, and grandparents that I lost in my adoption, and they lost me. I wondered, while watching this series, how many of those connections Beth had lost and if she’d ever re-establish them.

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Female empowerment is what most critics have focused on when they discuss the series, and of course, many chess players have weighed in on the accuracy of the games depicted. As Jennifer Shahade, two-time U.S. Chess Champion notes, she was not expecting complete accuracy and was more interested in the effect the show might have — namely, drawing more women and girls to chess, which so far, seems to be happening. However, as she notes, since Garry Kasparov and Bruce Pandolfini were asked to consult on the series, it turned out to be extremely realistic.

This one is a library for doing ML, similar to sklearn. Though it has UI, on which you can do your ML by clicking multiple mouse buttons. Import data, explore it, impute missing data if you’d like, and then even run AutoML, which will search on models and configuration to find the best one (also stacks them at the end!). I’d say it can help even a person having almost none programming experience to do ML.

After watching the Netflix drama mini-series, The Queen’s Gambit, I was struck by so much. The set design, the makeup and costumes, the characters, and of course, the chess. As a woman, I was in awe of the lead character, Beth Harmon, played by Anya Taylor-Joy. As much as I love chess, I was never trained to play as a child, partly because my family just didn’t play chess, but also, girls are far less likely to be introduced to the game, let alone encouraged to become great at it. To see a woman depicted not only as highly skilled, but dominating the male competition, is the kind of representation I am 100% here for.

Another thing the series depicted accurately that I have yet to see discussed is adoption. Not because it portrayed adoption as a wonderful solution to a supposed social dilemma, nor because it celebrated adoptive families as unique and wonderful. Quite the contrary. The Queen’s Gambit showcased the complexity of adoption from the adoptee’s perspective, something rarely, if ever done in popular film. The timing could not have been more perfect either, as we are smack in the middle of National Adoption Awareness Month (NAAM), a month that has become dominated by adoptive parents sharing how amazing adoption has been for them, with hardly any voice given to the children adoption is supposed to benefit.

Let’s do a hyperparameter tuning for them. Usual helpers are GridSearchCV (exhaustive search) or RandomSearchCV (not necessarily get the best combination) from sklearn. I was so glad that I found out about this amazing gem — scikit-optimize. It has BayesSearchCV. It takes ranges and takes a parameter of several iterations. It should try to optimize your model based on the scorer you provide. Minor issue with this that it could fall to a local minimum, and you wouldn’t improve anywhere.

and access your data in the usual way, just from /content/drive. Though I guess you agree with google seeing your data, which might not work for sensitive information. Though for Kaggle and playing around, it works quite well.

This snippet would fix imbalances, remove outliers and fit multiple models with a search on hyperparameters. My data was around 1GB CSV file, so even 5 folds take some time on one model. Keeping in mind that there is hyperparameter tunning multiple models 5 folds = too long waiting time. If I’d have smaller datasets, I’d definitely give it a try again; it seems easy to work with, and it does all the work for you!

Their framework is easy to work with, specifying layers similar to MLPClassifier in sklearn — pass a list with neuron count in each layer. Though running your training on CPU will drive you mad (it takes A LOT of time, like literally A LOT). In my case, when I used the CPU, it showed 150 hours. None of my owned machines had an Nvidia GPU inside, so it wasn’t on my list to try first. In later parts, I’ll tell how to use free GPU on some platforms and speed up any model training that can leverage GPU.

When Beth is 8-years-old, her mom dies, leaving her orphaned. However, she is not technically orphaned, and we learn later that her father is alive, but has refused to take responsibility for her. It turns out many adoptees are merely “paper orphans” and adoption agencies often make it seem like they are truly without parents or extended family to care for them, when this is far from the case.

The Queen’s Gambit does not sugarcoat Beth’s experience as an adoptee. While it is easy to get swept up in her genius and fame — I found myself idealizing her so often in the show — even her prodigious chess-playing does not come without great cost. This is what so many adoptees want you to know about adoption. For all its supposed greatness and happy family-building, adoption is, fundamentally, a great loss.

I was not too fond of that H2OFrame doesn’t quite work like pandas/spark/dask data frames sometimes. It was a bit hard to figure out how to do certain things, i.e., get a list of distinct column values. Here I had to use flatten, though in pandas would be list(). Nonetheless pretty cool library which can help anyone!

How many times you ran a model, got a good score, but the next day you couldn’t reproduce because you’ve made some changes here and there (different configuration, dataset)? At least to me, it was getting out of hand quite fast. Only printing some configuration, and the AUC score was getting lost pretty fast. I needed to track all of the configurations, scores, and data in general somehow. Using this platform, I could have logged my best model and then fine-tune it. Best discovery for me, though it was a bit too late for the challenge.

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hold with no money and no qualifications, Lee grew up into a restless short-tempered youth, wanted to earn money fast and see the world. He enlisted in the Royal Navy but had to pull out within a year due to a serious leg injury that left him invalidated.