Yelp Vancouver approached me to do a takeover of their Instagram account. I was be able to feature some of my favourite food spots as well as further expand my audience. It was a lot of fun and I’m really happy with how it turned out. Below are the stories I created for the takeover on April 30th 2019.

# Posts in *"Miscellaneous"*

# Foodora Campaigns

Foodora(food delivery company) first reached out to me in January 2018 to collaborate and help market their campaigns. Ever since then, I have done numerous promotional posts for them thus spreading awareness and increasing engagement. The most recent Valentine’s Day 2019 collaboration was a giveaway allowing my followers a chance to win 2 $50 vouchers to use on the app. Entrants were required to follow both @foodora_ca and myself which helped foodora grow real active followers who have an interest in Vancouver’s food scene. They were also asked to tag their friends in the comment section (1 tag = 1 entry) which allowed the campaign to spread quickly throughout the lower mainland.

In November 2018, foodora was approved to start delivering alcohol. This was a big deal! They offered free delivery all of November which would create an incentive to order beverages that month. This post played a large part in my own freelance photography career. It showed employers that I was able to do professional product shoots and in turn led me to work with various companies.

Here was my post to help kick start the launch:

7 Eleven X foodora

foodora formed a national partnership with 7Eleven and everyday essentials(milk, bread, Advil, condoms, Tylenol etc.) are now available for delivery exclusively through foodora Canada-wide. In addition, every order came with a 7Eleven x foodora tote bag.

My demographic is mostly students of Vancouver. When they announced that they delivered to The University of British Columbia, it was a BIG DEAL. This came in handy during exam season!

Pi (3.14) day was a really fun campaign and also my first! Who doesn’t love a good double meaning?! foodora worked with Australian company Peaked Pies to create an exclusive surf-and-turf pie ONLY AVAILABLE through the foodora app.

# MNIST Handwritten Digit Classifier

Dataset: Popular machine learning dataset from http://yann.lecun.com/exdb/mnist/ . Images are 24×24 pixels thus resulting in 784 explanatory variables. Each pixel will have a gray scale numerical value. The label/response variable is given from digits 1-9. Training set has 7000 observations while the test set is around 2000.

Goal: Use training/test set method in conjunction with ML algorithms to classify handwritten digits correctly.

We can try a **decision tree** first, it’s the easiest and can sometimes lead to good results.

```
library(rpart)
dtree <- rpart(as.factor(label) ~ ., data = dat.train, method='class')
plot(dtree, uniform=FALSE, margin=0.1)
text(dtree,use.n=F)
```

Each node makes a decision to split based on a threshold. It is safe to say that each split should result in the most homogeneous sub-tree.

I personally like to imagine a decision tree as one of those carnival games where you drop a marble into a box of pegs, but in this case the pegs are nodes deciding where the marble will fall.

With classifiers, MSPE is not appropriate. We can compare our predictions with the test set. The number of correctly classified digits divided by the total number of rows in our test data.

```
dtree.pr <- predict(dtree, newdata=dat.test, type='class')
dtree.pr.acc <- 1- sum(as.numeric(as.numeric(levels(dtree.pr))[dtree.pr]- as.numeric(dat.test$label) != 0))/nrow(dat.test)
dtree.pr.acc
```

#0.6165769 – so 61% success rate is not the best. This is to be expected. Decision trees are known to be unstable; a small change in the data could result in a COMPLETELY different tree.

So let’s try something else.

**Bagging / bootstrapping**

Now imagine I went up to 20 different magical talking decision trees and asked them what digit I have written on a piece of paper.

eight of them say the number 7 and the other twelve say it’s the number 9. We take the majority as our prediction. (for regression we can take the average response but this is classification!!)

We don’t have 20 different magical talking trees, so we need to make them. This process is called bootstrapping: randomly sample with replacement from our current training set to create 20 new pseudo-training sets. This can be computationally intense and could take a while.

`my.c <- rpart.control(minsplit = 3, cp = 1e-6, xval = 10)`

ensemble <- 20

ts <- vector('list',ensemble)

n <- nrow(dat.train)

for (j in 1:ensemble){

ii <- sample(1:n, replace=TRUE)

ts[[j]] <- rpart(label ~ ., data = dat.train[ii,], method = 'class', parms = list(split = 'information'), control= my.c)

}

Training each of the 20 training sets results in 20 different decision trees. This process further reduces variance.

It’s important to acknowledge that these trees seem over fit and we need to resist the urge of pruning, which is the process of simplifying trees by removing leaves/branches with low predictive power. In the case of bagging, an ensemble of overfit trees provides us with a better majority vote.

Let’s calculate our prediction error now:

`prs <- list() for(j in 1:ensemble) { pred <- predict(ts[[j]], newdata=dat.test, type='class') prs[[j]] <- as.matrix(pred) } prs.mx = do.call(cbind, prs) prs.bagg <- c() for (i in 1:dim(prs.mx)[1]){ prs.bagg <- c(prs.bagg, mode(prs.mx[i,])) }`

`bagg.pr.acc <- 1- sum(as.numeric(as.numeric(prs.bagg) - as.numeric(dat.test$label) != 0))/nrow(dat.test)`

#0.8445829 – around 84% success rate! Pretty good but we still have one more thing to try…

**Random Forest**

Random forest in my experience is usually the best option. Similar to bagging, we have an ensemble, however it attempts to break up correlation by randomly limiting the features available at each node in each tree. Some drawbacks of random forest is that we need to take a sufficiently large ensemble in order for all features to be included.

```
library(randomForest)
dat.tr <- as.factor(dat.train$label)
randomf <- randomForest(as.factor(label) ~ ., data = dat.train, ntree = 50)
```

```
randomf.pr <- predict(randomf, newdata=dat.test, type='class')
randomf.pr.acc <- 1-sum(as.numeric(as.numeric(levels(randomf.pr))[randomf.pr] - as.numeric(x.test$label) != 0))/nrow(dat.test)
```

#0.93465612 – 93% success rate!! Pretty good, I think we can stop there.

# Paris Pride

The last 4 months in Europe has truly changed me. Working and living abroad has rekindled my passion for what I do and allowed me to grow as a person.

I was fortunate enough to attend my first Pride event ever in Paris and it was one of the best experiences of my life. Attending this event invoked bittersweet emotions in me. It made me think of how far us as a society has come. I never expected so many people to come out to show their support. The event was a literal party: rainbow everything, hundreds of floats, music blasting, drag queens everywhere, people were celebrating love and inclusion. I was so moved. Below are videos and photos I took at the event:

# STAT 306 – Finding Relationships in Data Course Reflection

I also thought that the topics covered in this course will be very relevant at my future job. A lot of the course was done in R. There was an interesting homework assignment where he gave the entire class the same data set. Whoever was able to get the lowest Mean Squared Prediction Error with their model would get a high mark. We were able to model the data however we liked. I personally used a training/test subset approach.

Topics covered:

- Modeling a response variable as a function of several explanatory variables
- Multiple regression for a continuous response
- Logistic regression for a binary response
- Log-linear models for count data
- Finding low-dimensional structure
- Principal components analysis (PCA)
- Cluster analysis

# MATH 307 – Applied Linear Algebra

Definitely one of the harder courses. However, I came out with good MATLAB knowledge, which I used in other courses I was taking at the same time. The course felt like a combination of many of my past MATH courses but more in-depth and difficult.It always feels rewarding when you get to apply mathematical concepts to real world problems like chemical systems, circuits, and Markov Chain probabilities.

Topics Covered:

- Solving linear equations
- Interpolation
- Finite difference approximations
- Subspaces, basis and dimension
- The four fundamental subspaces for a matrix
- Graphs and networks
- Projections
- Complex vector spaces and inner product
- Orthonormal bases, orthogonal matrices and unitary matrices
- Fourier series
- Discrete Fourier transform
- Eigenvalues and Eigenvectors
- Hermitian matrices and real symmetric matrices
- Power method
- Recursion relations
- Markov chains
- Singular value decomposition
- Principle component analyis (PCA)
- Applications of linear algebra to problems in science and engineering
- Use of computer algebra systems for solving problems in linear algebras

# UBC Storm the Wall 2018

As my time at UBC was coming to an end, I ask myself “did I truly get the full University experience?”

The answer was no. So I made a pact with my close friends that we would say yes to various UBC events, join more clubs, and become more involved with school activities. This year I finally got to try UBC AMS’ annual Storm The Wall.

“Storm the Wall has been a lasting tradition at UBC starting in 1978. It has grown to be the **largest intramural event in North America** with over 800 teams registering! The race itself is similar to a triathlon, but completed as a relay with a team of five. One team member swims laps in the Aquatic Centre, the second does a sprint up to Main Mall, the third bikes around Main Mall, the fourth runs through campus to the wall, and the last teammate joins the team at the wall where every team member will climb over!” – *Recreation UBC*

I volunteered to be the long distance run through the campus and boy did I overestimate my physical abilities.

# Sum of all Natural Numbers = -1/12 !?

Quick post about something I came across the other day. I’m currently learning the Principle of Mathematical Induction(PMI) in MATH 220. It is essentially a way of proving a statement is true for all natural numbers. It has a lot to do with proving the summation of sets. However, I will talk more about the PMI in a different post.

A math meme page I follow on Facebook posted:

Image may contain: 4 people, people smiling

It’s a joke about the pineapple pen guy but in math terms. The meme is making fun of the theory/proof that the sum of all natural numbers converges or equals to -1/12 or 1+2+3+4+…+∞ = -1/12. I was really confused and I even believed it at the time! (it is obviously not true though)

I googled it and one of the most popular result is this video which “proves” the theory: https://www.youtube.com/watch?v=w-I6XTVZXww

I won’t go too in depth but the proof is incorrect due to it assuming many things that are not true. For starters, the video attempts to assume that 1-1+1-1+1… =1/2. The video does not explain why and assumes that it is common knowledge. However, assume 1-1+1-1+1… is a finite sum Z. Adding Z to itself you would get:

Z+Z=1-1+1-1+1…1-1+1-1+1… but this is just the original sum.

This implies Z+Z=2Z=Z and since Z=1/2, it follows that 1/2 = 1. Which is not possible. Therefore the video’s proof was flawed. This is just one of their incorrect assumptions. For more a more in-depth and thorough analysis, I definitely recommend reading this article: https://plus.maths.org/content/infinity-or-just-112

# Journey to Python – Introduction

Learning a new language or program can sometimes be a little intimidating. Foreign syntax, operators, and functions can be overwhelming and seem like a lot to take in. I’ve heard many great things about Python from my fellow coding friends. They explained to me that it would be a relatively easy and straightforward language to pick up, especially with my prior CPSC 210 Java knowledge. I find programming very interesting as it gives developers full reign and freedom. I like to compare a programmer and a language to an artist and a paintbrush. The possibilities of what one can do is only limited to his/her willingness to learn and the ability to express ideas creatively.

Because I have minimal Python knowledge, I will be learning the basics from multiple sources such as: LearnPython.org, YouTube, etc.

The first thing that came to my attention is that you do not need to declare variables and their type before using them. This is a new concept to me because in my past programming experience, objects had a variable name and declared type prior to their use.

Example in Java: String s1 = new String(“hello”)

Example in Python: s1 = “hello”

Also in Python, you are able to assign more than one variable at the same time using commas. However, I imagine this could get quite messy with more variables.

Example in Python: a, b = 3, 4 in which a is 3 and b is 4.

Some things to keep in mind: An exercise on LearnPython.org used the function isinstance(object, classinfo) https://docs.python.org/2/library/functions.html#isinstance

They also used the symbol % which is actually a string formatting operator but can also be use as modulus/remainder. Same as Math 220! https://docs.python.org/3/library/stdtypes.html#printf-style-string-formatting

%s – String (or any object with a string representation, like numbers)

%d – Integers

%f – Floating point numbers

You can also multiply strings with a number. Example: “hi” * 5 = “hihihihihi”

My first impressions: After working through simple introduction modules, it seems that Python and Java have many similarities. However, this is just the very beginning of Python for me and I am excited to see how the two differ as I get more in depth.