Deep Learning
Never giving up at the things you were told to do was the essence of work in the industrial age. Giving up quickly on things that are more trouble than they’re worth is what it takes to be successful today.
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2018 is looking busy, but there’s still a lot left from 2017 that I didn’t write about or wrap up.
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I’ve been curating my social media, as I do at the end of every year. By “curating” I mostly mean “deleting annoying rants” but also a whole lot of stuff that was timely when posted but irrelevant a week later. As I re-read things, some of them stand out, and are worth calling attention to.
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I left last week’s PyData Meetup with more questions than answers. Questions like “why does that neural net I just wrote perform the way it does?” So, with a couple of weeks left until the next project is due, I decided to go back and revisit the second half of the neural networks topic before moving forward.
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Tonight I joined the first Southern California PyData meetup. It featured two speakers discussing how to better understand the predictions made by machine-learning models, and why it might be important to do so. I was impressed by the capabilities of the packages demonstrated and the likely importance of having such capabilities as we move forward with deep learning-based automation that could cause catastrophic results if it fails in unexpected ways.
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There is little here that I could not learn on my own. But I find that it’s useful to learn along with others, and the structure that programs like this provide can be useful, so long as it isn’t too expensive. For myself, the structure and ability to discuss issues and problems with others were the key things that made this summer’s effort worth the $600.
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Yes, it’s the obligatory WordPress “hello world” post! Welcome to my blog.
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