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Getting started without NCS?

Hello all,

So as the subject line suggests, I don't yet have the NCS as yet, hopefully within the next few weeks. Mean time I want to keep some sort of momentum and learn stuff. I started with the Software install following instructions at [1]. Unfortunately the second step began with "Plug the NCS to the host machine" :-( I didn't think I needed the NCS for the compile of a network but there you go.

So in the mean time I'm a newbie. So what to do? I did find blog post about setting up caffe, [2] but that was a dumb list of steps without explaining what you were doing. I did read an article about caffe which had a prerequiste of knowing Google's Protocol Buffers so I read up on that. (Not sure the world needs another protocol for seralising and defining data structures but hey ho.). So then I found a list of books [3]. I started at title 3 "Understanding Machine Learning: From Theory to Algorithms" but there was a bit of maths formula in it so I should go back and do the first in the list "Think Stats: Probability and Statistics for Programmers". Oh the joy.

No I need to find a good intro to caffe, defining a simple network to solve an XOR dataset. Define it, compile it, train it, execute it. What are the options for configuring a network, the algorithms for feeding forward and backward, if that's the right terminology. What happens to my XOR Net if I change from a petrol engine to Diesel to Electric. And what about the gear box, (transmission for ye in the US). What exactly are the engine and gear box options? And what do they mean in practice. I'm not a Car Designer but if somebody asked me to design a car I'd have half a clue, of the bits you need, I don't have that info for Machine Learning.

Has somebody found that information out there? If so lend a hand here and point me to it. Sometimes putting together a good search term for the Internet is all you need and I've failed.

[1] https://developer.movidius.com/getting-started/compile-networks-using-the-toolkit
[2] https://www.oreilly.com/learning/how-to-build-and-run-your-first-deep-learning-network
[3] http://www.kdnuggets.com/2017/04/10-free-must-read-books-machine-learning-data-science.html

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