Coding, JavaScript, Machine Learning, Milestones, Murderous Maths, neural networks, Notes on everyday life, Set the beautiful mind free, Simon teaching, Simon's Own Code, Simon's sketch book

What Simon did instead of taking the SAT on Saturday

On Saturday morning, Simon didn’t go to the SAT examination location, although we had registered him to try taking the SAT (with great difficulties, because he is so young). In the course of the past few weeks, after trying a couple of practice SAT tests on the Khan Academy website, we have discovered that the test doesn’t reveal the depth of Simon’s mathematical talent (the tasks don’t touch the fields he is mostly busy with, like trigonometry, topology or calculus and require that instead, he solves much more primitive problems in a strictly timed fashion, while Simon prefers taking time to explore more complex projects). This is what happens with most standardized tests: Simon does have the knowledge but not the speed (because he hasn’t been training these narrow skills for hours on end as his older peers at school have). Nor does he have the desire to play the game (get that grade, guess the answers he deosn’t know), he doesn’t see the point. What did he do instead on his Saturday? He had a good night sleep (instead of having to show up at the remote SAT location at 8 a.m.) and then he…

built an A.I. applying a genetic algorithm, a neural network controlling cars moving on a highway! The cars use rays to avoid the walls of the highway. Implementing neuroevolution. What better illustration does one need to juxtapose true achievement and what today’s school system often wants us to view as achievemnt – getting a high grade on a test? The former is a beautiful page from Simon’s portfolio, showing what he really genuinely can do, a real life skill, something he is passionately motivated to explore deeper, without seeking a reward, his altruist contribution to the world, if you will. The latter says no more than how well one has been trained to apply certain strategies, in a competitive setting.

Simon’s code is online:

Simon has put this version on GitHub:

He has also created an improved version with an improved fitness function. “In the improved version, there’s a feature that only shows the best car (and you can toggle that feature on and off). And most importantly, I am now casting relative to where it’s going (so the linearity is gone, but it jiggles a lot, so I might linear interpolate it)”, – Simon explains. You can play with the improved version here:

Finally, Simon is currently working on a version that combines all the three versions: the original, the improved and the version with relative directions (work in progress):

“I am eventually going to make a version of this using TensorFlow.js because with the toy library I’m using now it’s surprisingly linear. I’m going to put more hidden layers in the network”.

The raytracing part of the code largely comes from Daniel Shiffman.

Simon’s two other videos about this project, that was fully completed in one day:

Part 1
Part 2

Coding, Community Projects, Contributing, JavaScript, Machine Learning, Milestones, Murderous Maths

Simon’s Decision Tree Library

Simon has just created a decision tree library, called “Decision”, that is helpful in building decision trees/forests (Machine Learning). He has also tried performing unit tests for the first time, and passed several of them! Once Simon’s library is in GitHub he also plans to link it to the testing hub CircleCI so that no merging can happen without passing tests. In this video, Simon explains what a decision tree is, shows his library and his test decision trees.

Simon’s library on GitHub (with a huge Readme that Simon wrote himself):

Simon’s library on CircleCI:

Simon’s unit tests:

Screen Shot 2018-02-20 at 16.59.12

Coding, JavaScript, Lingua franca, live stream, Machine Learning, Milestones, Murderous Maths, neural networks, Notes on everyday life, Python

Magic around New Year’s Eve

This magical time of the year, Simon’s craziest, most daring dreams come true! First, his guru from the New York University Daniel Shiffman sends Simon his book and the words he writes there are the most beautiful words anyone has ever told him. Then, on the last day of the awesome year 2017, Simon’s other hero, the glamorous knight of AI Siraj Raval materialises in our living room, directly from YouTube! Happy New Year full of miracles and discoveries everyone!


Daniel Shiffman’s book “The Nature of Code” that Simon had already largely read online and now also reads before bed. It also comforted him recently when he was in pain, he cuddled up of the sofa with this big friendly tome on his lap.


Daniel Shiffman signed the book for Simon:



Siraj Raval stepped out of the YouTube screen straight into our Antwerp apartment on December 31. Simon has been following Siraj’s channel for months, learning about the types of neural networks and the math behind machine learning. It is thanks to Siraj’s explanations that Simon has been able to build his first neural nets :



Schermafbeelding 2018-01-05 om 02.07.56

Coding, Machine Learning, Milestones, Murderous Maths, neural networks, Python, Simon teaching

The Neural Nets are here!

Simon has started building neural networks in Python! For the moment, he has succeeded in making two working neural nets (a Perceptron and a Feed Forward neural net). He used the sigmoid activation function for both. The code partially derived from Siraj Raval’s “The Math of Intelligence” tutorials.

ML Perceptron 10 Dec 2017

The FF was tougher to build:

Simon’s nets run locally (on our home pc), but he will need more computational power for the more complex future projects, so he signed up to this wonderful online resource called FloydHub! FloydHub is sort of a heroku for deep learning, a Platform-as-a-Service for training and deploying deep learning models in the cloud. It uses Amazon, which Simon could, too, but it would have been a lot more expensive and tedious work to set up.

Simon’s next step will be another supervised learning project, a Recurrent Neural Net that will generate text. He has already started building it and fed it one book to read! In this video he explains how character-based text generators work:

Coding, Java, Machine Learning, Milestones, Murderous Maths, Simon teaching

Simon explains K Means Clustering

Simon has prepared this implementation of “K-Means Clustering” in Processing as a gift for Daniel Shiffman, who is plainning to talk about this Machine Learning model in one of his upcoming live sessions on the Coding Train channel.

Simon writes: K-Means Clustering is a type of Machine Learning Model. It’s for “Unsupervised Learning” (meaning you have data with no labels).

Link to Simon’s code on GitHub:

Link to pseudocode by Siraj Raval:



Coding, Milestones, neural networks, Python, Simon teaching

Simon working on a neural networks paper

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”.


Neural Networks Paper Jupyter 2017-11-20 1

Neural Networks Paper Jupyter 2017-11-20 2

Neural Networks Paper Jupyter 2017-11-20 3

Neural Networks Paper Jupyter 2017-11-20 4

Coding, Java, Murderous Maths, Simon's Own Code

Simon’s own little neural network

Connected Perceptrons in Processing 26 Jul 2017

This is one of Simon’s most enchanting and challenging projects so far: working on his own little AIs. As I’ve mentioned before, when it comes to discussing AI, Simon is both mesmerized and frightened.  He watches Daniel Shiffman’s neural networks tutorials twenty times in a row and practices his understanding of the mathematical concepts   underlying the code (linear regression and gradient descent) for hours. Last week, Simon built a perceptron of his own. It was based on Daniel Shiffman’s code, but Simon added his own colors and physics, and played around with the numbers and the bias. You can see Simon working on this project step by step in the six videos below.

His original plan was to build two neural networks that would be connected to each other and communicate, he has only built one perceptron so far.







Coding, CSS, html, JavaScript, Milestones, Murderous Maths, Physics

Simon gets serious with Linear Regression (Machine Learning)

Simon has been working on a very complicated topic for the past couple of days: Linear Regression. In essence, it is the math behind machine learning.

Simon was watching Daniel Shiffman’s tutorials on Linear Regression that form session 3 of his Spring 2017 ITP “Intelligence and Learning” course (ITP stands for Interactive Telecommunications Program and is a graduate programme at NYU’s Tisch School of the Arts).

Daniel Shiffman’s current weekly live streams are also largely devoted to neural networks, so in a way, Simon has been preoccupied with related stuff for weeks now. This time around, however, he decided to make his own versions of Daniel Shiffman’s lectures (a whole Linear Regression playlist), has been busy with in-camera editing, and has written a resume of one of the Linear Regression tutorials (he actually sat there transcribing what Daniel said) in the form of an interactive webpage! This Linear Regression webpage is online at: and the Gragient Descent addendum Simon made later is at: and

And here come the videos from Simon’s Liner Regression playlist, the first one being an older video you may have already seen:

Here Simon shows his interactive Linear Regression webpage:

A lecture of Anscombe’s Quartet (something from statistics):

Then comes a lecture on Scatter Plot and Residual Plot, as well as combining Residual Plot with Anscombe’s Quartet, based upon video 3.3 of Intelligence and Learning. Simon made a mistake graphing he residual plot but corrected himself in an addendum (end of the video):

Polynomial Regression:

And finally, Linear Regression with Gradient Descent algorithm and how the learning works. Based upon Daniel Shiffman’s tutorial 3.4 on Intelligence and Learning: