Simon was working on a neural networks paper in Jupyter Notebook on Friday evening, but didn’t finish it because the Coding Train live stream started. He says he can no longer continue without having to do too much copy-pasting from this version into a new one, as his in-your-browser time expired, so I’m posting some screen shots of the unfinished paper below. This is the way Simon teaches himself: he follows lectures and tutorials online and then goes ahead to writing his own “textbook”or recording his own “lecture”. Much of the knowledge he acquires on neural networks these days comes from Siraj Raval’s YouTube series “The Math of Intelligence”.
Simon has already built a Perceptron before, several months ago, while following along with Daniel Sgiffman’s Coding Train channel. This time around, he is writing his own code ad doing all the matrix calculations himself. He hasn’t finished programming this network yet, but it’s a good start:
Doing Matrices in Khan Academy’s Precalculus course:
There’s a part 3 coming!
“Mom, my ClickCharts trial period expired, so I found this Virtual Paradigm Enterprise!” (Simon independently searches for free options to make beautiful diagrams online).
Here a diagram of an LSTM neural network:
And an RNN:
Simon loves looking at things geometrically. Even when solving word problems, he tends to see them as a graph. And naturally, since he started doing more math related to machine learning, graphs have occupied an even larger portion of his brain! Below are his notes in Microsoft Paint today (from memory):
Slope of Line:
Steepness of Curve:
An awesome calculator Simon discovered online at desmos.com/calculator that allows you to make mobile and static graphs:
Yesterday’s notes on the chi function (something he learned through 3Blue1Brown‘s videos on Taylor polynomials):
Simon following The Math of Intelligence course by Siraj Raval:
Simon explaining how a Denoising Auto Encoder (DAE) neural network works:
Today is a big day as – for the first time in human history – a short story has been published that was written by a robot together with a human. And the bot (called AsiBot, because it writes in the style of Isaac Asimov’s I, Robot) was developed in Dutch (!) in Amsterdam (at Meertens Institute) and in Antwerp (at the Antwerp Centre for Digital Humanities and Literary Criticim), Simon’s two home cities.
The story written by the AsiBot and Dutch bestselling author Ronald Giphart forms a new, 10th chapter in Isaac Asimov’s classic I, Robot (that originally contained only 9 chapters). The AsiBot was fed 10 thousand books in Dutch to master the literary language and can already produce a couple of paragraphs on its own, but a longer coherent story remains out of fetch. This is where a human writer, Ronald Giphart stepped in. It was he who decided which of the sentences written by AsiBot stayed and which should be thrown out. The reader doesn’t know which sentences are written (or edited) by the human writer and which are pure robot literature. Starting from November 6 anyone (speaking Dutch) can try writing with AsiBot on www.asibot.nl.
Simon was very excited about this news and recorded a short video where he explains how such “synthetic literature”neural nets work (based on what he learned from Siraj Raval’s awesome YouTube classes):
My phone froze so we had to make the second part as a separate video:
Simon has been watching a lot of Siraj Raval’s videos on neural networks lately, brushing up his Python syntax and derivatives. He has even been trying the great Jupyter editor, where one can build one’s own neural network and install libraries with pretrained networks https://try.jupyter.org/
Just like with Danel Shiffman’s videos, the remarkable thing about Siraj’s (very challenging) courses is that they also touch upon so many subjects outside programming (like art and music and stock exchange) and are compiled with a sublime sense of humour.