Hello,
there is a new version of the library for machine learning with Snap, this time organized as a collection of 6 sprites, which are called "Arthur&Ina". They contain the necessary operations for mathematics (linear algebra), image and data processing, SQL queries and fully wired neural networks of perceptrons. There is also a manual of 70 pages with descriptions of the new blocks as well as application examples, e.g. a convolutional neural network.
Have fun with it!
Eckart
I'm curious how large a neural network can it train in a reasonable amount of time. I ask this because in my work I rely upon tensorflow.js which scales very well but unlike your work introduces many "black boxes". I really like how you visualise the neural nets (but again am unsure how well it scales).
You should put some effort into generating error messages. For example the following works fine:
but there is no error message from this:
Table of contents contains a little bit of German still - "das" and "und". One chapter has "Anwendungen der ML.Sprites" as header.
"das" = the
"und" = and
"Anwendungen der ML.Sprites" = use of the ML.Sprites (also: applications of the ML.Sprites, don't know the context)
"accus" = ???
I proceeded to watch a list of the interview sections with him telling his life story.
I have especially liked him explaining the role that confidence has had in his developing interest to learn computer programming:
It would be really cool, if you @emodrow (or your students) would publish a video, in which you (or your students) would implement a task from a chapter from your PDF step by step.
For example the "Traffic sign recognition" task (from the page 46):
uploading images of 12 different traffic signs (choosing 12 because you want to have the same amount of possible labels as the length of input vector, per 'step 3' below - if I understand correctly);
reducing them to 100 x 100 pixels each;
using a "mean-pooling" in order to reduce them further to 2x2 pixels, each represented by three (RGB) values, resulting in the following = 4 pixels multiplied by RGB 'vector' (i.e. of length 3) = 12 as a length of an input vector;
applying the Softmax function to the input vector;
running the "learning runs" 50 times with higher learning rate and another 50 with low one for fine tuning;
succeeding.
P.S.
Oh, is there any particular reason that you chose to use "mean-pooling" (instead of "max-pooling") in the above task with traffic signs?
Hi,
max-pooling is more about the existence of a feature (there is a vertical line in the pooling-area), mean-pooling is more about its distribution (here: the colors). That seems more useful for the problem.
