Interesting article, is it concluding that different small networks are formed for different types of problems that we are trying to solve with the larger network?
How is this different from overfitting though? (PS: Overfitting isn't that bad if you think about it, as long as the test dataset or inference time model is trying to solve problems in the supposedly large enough training dataset)
Such an insightful article. The tools are allowing us to 10x-100x productivity in shorter bursts, which makes total sense. There's a lot more to software engineering beyond those bits, and that's why the 10x engineer imposter syndrome.
Pretty cool use of NLP. I wonder if training schools can use this to train their students to improve their readback detection. IMO, one of the hardest things as a pilot in training in the first few months is getting feedback on my communication with the tower.
How does EB-2 NIW essentially work? Is it tied to a specific employer or can it be used to justify H1b renewal/transfer beyond the 6 year limit at a different workplace with the different role ?
Looking for applied research opportunities. Previously worked on building flight software for eVTOLs and sensor evaluation for autonomous flight. Currently focused on sensing for consumer health research.
Likes to work at the intersection of product, hardware and software.
5 years of professional experience developing systems and applied research on various sensing modalities. Your go-to buddy for hacking out prototypes for applied research. My previous work involves building fault tolerant systems for eVTOLs, and currently involved in consumer health research. My firmware background coupled with strong data analytic skills have been useful in figuring out complex physical systems and unique bugs they encompass!
5 years of professional experience developing systems and applied research on various sensing modalities. Your go-to buddy for hacking out prototypes for applied research.
My previous work involves building fault tolerant systems for eVTOLs, and currently involved in consumer health research.
My firmware background coupled with strong data analytic skills have been useful in figuring out complex physical systems and unique bugs they encompass!
If you're learning a scripting language, building a web scraper is a great way to learn some fundamentals of a language by using the basic data structures, getting the data flow correct and solving regex, parsing and printing issues.
Protip: use an IDE and a debugger to turbocharge your lesson!
Great point about how encoding-decoding custom objects/models to JSON is the biggest pain point. Coming with requirements before starting out the Object->JSON encoding, really helps you solve the more complex bottlenecks you might hit in the later "serialization".
Its super interesting especially when you are using floating points, dates, C-style unions (represented as nested lists/dicts).
Interesting idea, though I am wondering how you are going to scale since games involve live hosts? Rather you could use this as a platform to connect "live hosts" (who make up and publish their games) with companies wanting to connect.
You could provide platform, services, payment gateway etc.
Its super important to focus on your career based on your interests, rather than recruiters priorities.
The author has some point in there, however, focussing on side projects does help you expand and broaden your experiences. Its supremely helpful when switching between slightly different industries.
How is this different from overfitting though? (PS: Overfitting isn't that bad if you think about it, as long as the test dataset or inference time model is trying to solve problems in the supposedly large enough training dataset)