Pic from Peace Arch News

How much time will it take to cross the border at Peace Arch?

Peng Wang
Living in Vancouver, it is so convenient driving cross the border and have some fun on the other side. However, if you have headache of waiting in the long border-crossing lines and getting stuck for almost an hour, you are not alone. We all know the basic strategies on best/worst days/hours to cross, for example, avoid long weekend or Christmas week, arriving the border early, etc. A crystal ball that can tell us ahead of time on our wait time at the border crossing will be just fantastic! Well, I decided to give it a swing and make a crystal ball - to build a machine learning model. Below is a quick summary of the workflow on this mini project. [caption id="attachment_512" align="alignnone" width="544"] Project workflow[/caption]
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Why so many wildfires in BC lately

Peng Wang
[caption id="attachment_478" align="aligncenter" width="623"] Downtown Vancouver before and after hazy smokes caused by massive wildfires[/caption] British Columbia wildfires are burning out of control! There were a number of air quality warnings issued cross BC in summer 2018. Smoky air flew all the way to Alberta and even cross the border to US. It made me curious what's going on with BC wildfires and what the situation was used to be. Wildwire datasets for previous years and current year (updated in May 2018) were gathered from DataBC, data published by BC Wildfire Service. For relevance and clarity, data prior to 1980 is ignored.

Build CNN for facial expression recognition with TensorFlow Eager on Google Colab

Peng Wang
Key learning elements:

» Run experiments in Google Colab and access files on Google Drive

» Build and evaluate a model using Tensorflow Eager mode

» Build a Convolutional Neural Network (CNN) to recognize 7 facial expressions

For this exercise we are going to build a CNN for facial expression recognition on fer2013 dataset, available on Kaggle. fer2013 is a publicly accessible, and it contains 35,887 grayscale, 48 x 48 sized face images with 7 emotional expressions: angry, disgust, fear, happy, sad, surprise, and neutral. It was originally published on International Conference on Machine Learning (ICML) 2013, Challenges in Representation Learning: A report on three machine learning contests, Ian Goodfellow et al., 2013