i watch all video and read some of paper to understand HTM theory but i am so confuse and i think i dont understand correctly. whoud you mind explain me this theory in simple word, Especially spatial pooling and temporal memory
thanks a lot
There is nothing simpler than HTM School at the moment. I suggest you watch the relevant episodes again. Have you read BAMI?
thanks, so do I. you know when I whatch video I think to implementation and sometimes I do not understand if I want run the code , what are this matrix and etc
no can you send BAMI to me?
thanks a lot, you are great
Hey @shiva, for implementation you may want to have a look at this new python notebook. It’s a lot like the existing API demo with some additional stuff. You can implement the encoding, SP and TM algorithms piece by piece:
thank you very much, I really like run this demo but I am Beginner and I do not work python before, would you mind explain me what should I do to run this code?
for example which version of python I should install and then how run this code?
You need to download and install python 2.7.15 https://www.python.org/downloads/release/python-2715/
Then from a terminal or cmd prompt run:
pip install nupic
From there you can run the examples in the quick start guide: http://nupic.docs.numenta.org/stable/quick-start/index.html
But first, you should probably learn a bit more about programming before diving into something like this.
Perhaps HTM Studio would be a better option for you: https://numenta.com/machine-intelligence-technology/htm-studio/
Thank you very much! but i didnt work with Jupyter Notebook, I would be grateful if you could help me and explain what should I do to run this code?
I’m Nili who has been familiar with HTM for some time, have read its articles and books, and have been able to develop HTM on .NET and in C# and C ++. Email me if you wish (firstname.lastname@example.org)
I understand your pain. I don’t know what the question was, but Python is not the answer.
I’d be interested to take a look, but you really need to publish it on GitHub or similar.