Udacity Deep Learning Nanodegree — Part 3

I found TensorFlow initially confusing but then quite comfortable. It’s odd how after programming in a language like Python for a while, it becomes confusing that you have to declare “placeholders” (variables) and constants up-front, then initialize them.

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It’s not just a problem “over there.”

The American Red Cross is running computers with an obsolete and unsupported operating system, and using them to collect HIPAA protected personal health and other information! If a Russian or North Korean hacker can break your system because you’re using 16 year-old software that hasn’t been supported for two years, then you as an organization have failed.

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Udacity Deep Learning Nanodegree – Part II

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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PyData Socal: Explaining Black Box ML Predictions

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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Udacity Deep Learning Nanodegree – Part I

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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