Phil 12.2.15

7:00 –

  • Learning: Neural Nets, Back Propagation
    • Synaptic weights are higher for some synapses than others
    • Cumulative stimulus
    • All-or-none threshold for propagation.
    • Once we have a model, we can ask what we can do with it.
    • Now I’m curious about the MIT approach to calculus. It’s online too: MIT 18.01 Single Variable Calculus
    • Back-propagation algorithm. Starts from the end and works forward so that each new calculation depends only on its local information plus values that have already been calculated.
    • Overfitting and under/over damping issues are also considerations.
  • Scrum meeting
  • Remember to bring a keyboard tomorrow!!!!
  • Checking that my home dev code is the same as what I pulled down from the repository
    • No change in definitelytyped
    • No change in the other files either, so those were real bugs. Don’t know why they didn’t get caught. But that means the repo is good and the bugs are fixed.
  • Validate that PHP runs and debugs in the new dev env. Done
  • Add a new test that inputs large (thousands -> millions) of unique ENTITY entries with small-ish star networks of partially shared URL entries. Time view retrieval times for SELECT COUNT(*) from tn_view_network_items WHERE network_id = 8;
    • Computer: 2008 Dell Precision M6300
    • System: Processor Intel(R) Core(TM)2 Duo CPU T7500 @ 2.20GHz, 2201 Mhz, 2 Core(s), 2 Logical Processor(s), Available Physical Memory 611 MB
    • 100 is 0.09 sec
    • 1000 is 0.14 sec
    • 10,000 is 0.84 sec
    • Using Open Office’s linear regression function, I get the equation t = 0.00007657x + 0.733 with an R squared of 0.99948.
    • That means 1,000,000 view entries can be processed in 75 seconds or so as long as things don’t get IO bound
  • Got the PHP interpreter and debugger working. In this case, it was just refreshing in settings->languages->php

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