AlphaGo Zero: An overview of the algorithm

Jessica YungArtificial Intelligence, Highlights

In this post I go through the algorithms presented in the groundbreaking AlphaGo Zero paper using pseudocode. The objective is to provide a high-level idea of what the model does. Why AlphaGo Zero matters Last week, Google DeepMind published their final iteration of AlphaGo, AlphaGo Zero. To say its performance is remarkable is an understatement. AlphaGo Zero made two breakthroughs: … Read More

Behavioural Cloning: Tips for Tackling Project 3

Jessica YungSelf-Driving Car ND

In this post I list tips that may be helpful for tackling Project 3 of Udacity’s Self-Driving Car Nanodegree, in which you train a neural network to drive a car in a simulator. The neural network learns from data of humans driving the car through the simulator, hence the project name ‘Behavioural Cloning’ – it’s trying to imitate the way … Read More

Explaining TensorFlow code for a Multilayer Perceptron

Jessica YungHighlights, Programming, Self-Driving Car ND

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 YungData Science, Self-Driving Car ND, Statistics

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 YungSelf-Driving Car ND, Statistics

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

Ian Goodfellow: Generative Adversial Networks

Jessica YungData Science, Highlights, Talk Reviews

The second talk I went to at AI WithTheBest 2016 was Ian Goodfellow’s talk on Generative Adversarial Networks (GANs), which he invented. Ian is a researcher at OpenAI. GANs are generative models based on supervised learning and game theory. They learn to generate realistic samples and have mostly been used to generate images. For example, you can feed it images … Read More