## Mapping Economics

Jessica Yung

Scroll down for sample diagrams. Motivation: How can I contribute with my meagre knowledge of Economics? When you spend over twenty hours a week studying, you cannot help but ask, ‘Am I going to use a significant proportion of this later on in life, especially if I don’t become an economist? (No.) If not, why am I spending time learning … Read More

## Bugger! 4.1: Zebra Stripes for Lane Lines

Jessica Yung

In Project 4: Advanced Lane Lines of Udacity’s Self-Driving Car Nanodegree, we use computer vision techniques to trace out lane lines from a video taken from a camera mounted at the front of a car, like so: Result I’m supposed to get In this series, I share some bugs I came across and how I tackled them. The code can … Read More

## A critique of the Ellsberg Paradox

Jessica YungEconomics

Ellsberg Paradox We discussed the following version of the Ellsberg paradox in a microeconomics lecture yesterday: There is an urn with 100 red balls and 200 balls that are each either blue or green. (e.g. may have 100 red and 200 blue or 100 red, 199 green and 1 blue.) You have two choices. Choice 1: (a)  Receive 1,000 if … Read More

## List Comprehensions in Python

Jessica YungProgramming

1. What are list comprehensions? List comprehensions construct lists in natural-to-express ways. They can replace map-filter combinations and many for loops. They’re just syntactic sugar. That means they make your code easier to read (and prettier). Example 1: For loops -> List comprehension

Example 2: Map-filter -> List Comprehension

(I will do a post on map and filter and … Read More

## Code, Explained: Training a model in TensorFlow

Jessica Yung

In a previous post, we went through the TensorFlow code for a multilayer perceptron. Now we will discuss how we train the model with TensorFlow, specifically in a TensorFlow Session. We will use Aymeric Damien’s implementation in this post. I recommend you skim through the code first and have the code open in a separate window. I have included the key portions … Read More

## Comparing model performance: Including Max Pooling and Dropout Layers

Jessica YungSelf-Driving Car ND

In this post I compare the performance of models that use max pooling and dropout in the convolutional layer with those that don’t. This experiment will be on a traffic sign classifier used in Udacity’s Self-Driving Car Nanodegree. The full code is on GitHub. Recap: Max Pooling and Dropout Max Pooling: A way of reducing the dimensionality of input (by … Read More

## Explaining TensorFlow code for a Multilayer Perceptron

Jessica Yung

In this post we go through the code for a multilayer perceptron in TensorFlow. We will use Aymeric Damien’s implementation. I recommend you have a skim before you read this post. I have included the key portions of the code below. 1. Code Here are the relevant network parameters and graph input for context (skim this):

Here is the model … Read More

## Comparing Model Performance with Normalised vs standardised input (Traffic Sign Classifier)

Jessica Yung

In the previous post, we explained (1) what normalisation and standardisation of data were, (2) why you might want to do it and (3) how you can do it. In this post, we’ll compare the performance of one model on unprocessed, normalised and standardised data. We’d expect using normalised or standardised input to give us higher accuracy, but how much better … Read More

## Traffic Sign Classifier: Normalising Data

Jessica Yung

In this post, we’ll talk about (1) what normalising data is, (2) why you might want to do it, and (3) how you can do it (with examples). Background: The Mystery of the Horrifically Inaccurate Model Let me tell you a story. Once upon a time, I trained a few models to classify traffic signs for Udacity’s Self-Driving Car Nanodegree. I first … Read More

## 18 Game Theory Ideas

Jessica YungEconomics

Image creds: SMBC Here are 18 game theory-related ideas I came up with in the last Game Theory lecture of term. These are things that I think would be interesting to explore and are suited to (but do not require) people who have elementary knowledge of game theory. Look into Quantum game theory. Create a game theory problems tree. E.g. … Read More