Posts by Tag

deep_learning

Glow-TTS

April 11 2022

Note: This blog post was completed as part of Yale’s CPSC 482: Current Topics in Applied Machine Learning.

Score Matching

December 26 2021

Recently, I’ve heard a lot about score-based networks. In this post, I will attempt to provide a high-level overview of what scores are and how the concept o...

Flow Models

June 21 2021

In this post, we will take a look at Flow models, which I’ve been obsessed with while reading papers like Glow-TTS and VITS. This post is heavily based on th...

From ELBO to DDPM

May 17 2021

In this short post, we will take a look at variational lower bound, also referred to as the evidence lower bound or ELBO for short. While I have referenced E...

Relative Positional Encoding

March 01 2021

In this post, we will take a look at relative positional encoding, as introduced in Shaw et al (2018) and refined by Huang et al (2018). This is a topic I me...

BERT’s Common Sense, or Lack Thereof

February 18 2021

A few days ago, I came across a simple yet nonetheless interesting paper, titled “NumerSense: Probing Numerical Commonsense Knowledge of Pre-Trained Language...

GPT from Scratch

February 15 2021

These days, I’m exploring the field of natural language generation, using auto-regressive models such as GPT-2. HuggingFace transformers offers a host of pre...

NLI Models as Zero-Shot Classifiers

February 10 2021

In the previous post, we took a look at how to extract keywords from a block of text using transformer models like BERT. In that blog post, you might recall ...

Keyword Extraction with BERT

February 05 2021

I’ve been interested in blog post auto-tagging and classification for some time. Recently, I was able to fine-tune RoBERTa to develop a decent multi-label, m...

NLG with GPT-2

February 01 2021

When GPT-3 was released, people were amazed by its ability to generate coherent, natural-sounding text. In fact, it wasn’t just text; it could generate JavaS...

Attention is All You Need

January 20 2021

Today, we are finally going to take a look at transformers, the mother of most, if not all current state-of-the-art NLP models. Back in the day, RNNs used to...

Attention Mechanism

January 15 2021

Attention took the NLP community by storm a few years ago when it was first announced. I’ve personally heard about attention many times, but never had the ch...

(Attempt at) Knowledge Distillation

January 08 2021

For the past couple of months or so, I’ve been spending time looking into transformers and BERT. Transformers are state of the art NLP models that are now re...

Fast Gradient Sign Method

January 05 2021

In today’s post, we will take a look at adversarial attacks. Adversarial attacks have become an active field of research in the deep learning community, for ...

Better seq2seq

December 27 2020

In the previous post, we took a look at how to implement a basic sequence-to-sequence model in PyTorch. Today, we will be implementing a small improvement to...

Introduction to seq2seq models

December 20 2020

For a very long time, I’ve been fascinated by sequence-to-sequence models. Give the model a photo as input, it spits out a caption to go along with it; give ...

Neural Style Transfer

December 10 2020

In today’s post, we will take a look at neural style transfer, or NMT for short. NMT is something that I first came across about a year ago when reading Fran...

GAN in PyTorch

November 30 2020

In this blog post, we will be revisiting GANs, or general adversarial networks. This isn’t the first time we’ve seen GANs on this blog: we’ve implemented GAN...

InceptionNet in PyTorch

November 14 2020

In today’s post, we’ll take a look at the Inception model, otherwise known as GoogLeNet. I’ve actually written the code for this notebook in October 😱 but wa...

VGG PyTorch Implementation

November 01 2020

In today’s post, we will be taking a quick look at the VGG model and how to implement one using PyTorch. This is going to be a short post since the VGG archi...

PyTorch RNN from Scratch

October 25 2020

In this post, we’ll take a look at RNNs, or recurrent neural networks, and attempt to implement parts of it in scratch through PyTorch. Yes, it’s not entirel...

PyTorch, From Data to Modeling

October 20 2020

These past few weeks, I’ve been powering through PyTorch notebooks and tutorials, mostly because I enjoyed the PyTorch API so much and found so many of it us...

PyTorch Tensor Basics

October 10 2020

This is a very quick post in which I familiarize myself with basic tensor operations in PyTorch while also documenting and clarifying details that initially ...

BLEU from scratch

September 21 2020

Recently, I joined the Language, Information, and Learning at Yale lab, led by Professor Dragomir Radev. Although I’m still in what I would consider to be th...

A PyTorch Primer

September 14 2020

I’ve always been a fan of TensorFlow, specifically tf.keras, for its simplicity and ease of use in implementing algorithms and building models. Today, I deci...

Word2vec from Scratch

July 13 2020

In a previous post, we discussed how we can use tf-idf vectorization to encode documents into vectors. While probing more into this topic and geting a taste ...

Dissecting LSTMs

June 02 2020

In this post, we will revisit the topic of recurrent neural networks, or RNNs. Although we have used RNNs before in a previous post on character-based text p...

The Math Behind GANs

March 15 2020

Generative Adversarial Networks refer to a family of generative models that seek to discover the underlying distribution behind a certain data generating pro...

My First GAN

February 25 2020

Generative models are fascinating. It is no wonder that GANs, or General Adversarial Networks, are considered by many to be where future lies for deep learni...

A Step Up with Variational Autoencoders

February 22 2020

In a previous post, we took a look at autoencoders, a type of neural network that receives some data as input, encodes them into a latent representation, and...

So What are Autoencoders?

February 18 2020

In today’s post, we will take yet another look at an interesting application of a neural network: autoencoders. There are many types of autoencoders, but the...

A Simple Autocomplete Model

February 10 2020

You might remember back in the old days when autocomplete was just terrible. The suggestions provided by autocomplete would be useless if not downright stupi...

Building Neural Network From Scratch

February 05 2020

Welcome back to another episode of “From Scratch” series on this blog, where we explore various machine learning algorithms by hand-coding them from scratch....

Convolutional Neural Network with Keras

February 01 2020

Recently, a friend recommended me a book, Deep Learning with Python by Francois Chollet. As an eager learner just starting to fiddle with the Keras API, I de...

First Neural Network with Keras

January 15 2020

Lately, I have been on a DataCamp spree after unlocking a two-month free unlimited trial through Microsoft’s Visual Studio Dev Essentials program. If you hav...

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statistics

Glow-TTS

April 11 2022

Note: This blog post was completed as part of Yale’s CPSC 482: Current Topics in Applied Machine Learning.

Score Matching

December 26 2021

Recently, I’ve heard a lot about score-based networks. In this post, I will attempt to provide a high-level overview of what scores are and how the concept o...

Flow Models

June 21 2021

In this post, we will take a look at Flow models, which I’ve been obsessed with while reading papers like Glow-TTS and VITS. This post is heavily based on th...

From ELBO to DDPM

May 17 2021

In this short post, we will take a look at variational lower bound, also referred to as the evidence lower bound or ELBO for short. While I have referenced E...

Rejection Sampling

January 25 2021

In today’s post, we will take a break from deep learning and turn our attention to the topic of rejection sampling. We’ve discussed the topic of sampling som...

Beta, Bayes, and Multi-armed Bandits

August 28 2020

Recently, I fortuitously came across an interesting blog post on the multi-armed bandit problem, or MAB for short. I say fortuitous because the contents of t...

Gaussian Mixture Models

August 01 2020

We’ve discussed Gaussians a few times on this blog. In particular, recently we explored Gaussian process regression, which is personally a post I really enjo...

The Gibbs Sampler

June 12 2020

In this post, we will explore Gibbs sampling, a Markov chain Monte Carlo algorithm used for sampling from probability distributions, somewhat similar to the ...

Natural Gradient and Fisher

May 27 2020

In a previous post, we took a look at Fisher’s information matrix. Today, we will be taking a break from the R frenzy and continue our exploration of this to...

Fisher Score and Information

April 11 2020

Fisher’s information is an interesting concept that connects many of the dots that we have explored so far: maximum likelihood estimation, gradient, Jacobian...

On Expectations and Integrals

April 05 2020

Expectation is a core concept in statistics, and it is no surprise that any student interested in probability and statistics may have seen some expression li...

Stirling Approximation

April 01 2020

It’s about time that we go back to the old themes again. When I first started this blog, I briefly dabbled in real analysis via Euler, with a particular focu...

Principal Component Analysis

March 22 2020

Principal component analysis is one of those techniques that I’ve always heard about somewhere, but didn’t have a chance to really dive into. PCA would come ...

Fourier Series

March 19 2020

Taylor series is used in countless areas of mathematics and sciences. It is a handy little tool in the mathematicians arsenal that allows us to decompose any...

The Math Behind GANs

March 15 2020

Generative Adversarial Networks refer to a family of generative models that seek to discover the underlying distribution behind a certain data generating pro...

MLE and KL Divergence

March 09 2020

These days, I’ve been spending some time trying to read published research papers on neural networks to gain a more solid understanding of the math behind de...

Convex Combinations and MAP

January 25 2020

In a previous post, we briefly explored the notion of maximum a posteriori and how it relates to maximum likelihood estimation. Specifically, we derived a ge...

The Exponential Family

January 22 2020

Normal, binomial, exponential, gamma, beta, poisson… These are just some of the many probability distributions that show up on just about any statistics text...

Maximum A Posteriori Estimation

December 28 2019

In a previous post on likelihood, we explored the concept of maximum likelihood estimation, a technique used to optimize parameters of a distribution. In tod...

Demystifying Entropy (And More)

December 21 2019

The other day, my friend and I were talking about our mutual friend Jeremy. “He’s an oddball,” my friend Sean remarked, to which I agreed. Out of nowhere, Je...

Moments in Statistics

December 16 2019

The word “moment” has many meanings. Most commonly, it connotes a slice of time. In the realm of physics, moment refers to the rotational tendency of some ob...

Dissecting the Gaussian Distribution

December 12 2019

If there is one thing that the field of statistics wouldn’t be complete without, it’s probably normal distributions, otherwise referred to as “the bell curve...

Wonders of Monte Carlo

December 03 2019

I have been putting off with blog postsings lately, largely because I was preoccupied with learning new languages I decided to pick up out of whim. Although ...

A sneak peek at Bayesian Inference

November 30 2019

So far on this blog, we have looked the mathematics behind distributions, most notably binomial, Poisson, and Gamma, with a little bit of exponential. These ...

0.5!: Gamma Function, Distribution, and More

November 22 2019

In a previous post, we looked at the Poisson distribution as a way of modeling the probability of some event’s occurrence within a specified time frame. Spec...

How lucky was I on my shift?

November 20 2019

At the Yongsan Provost Marshall Office, I receive a wide variety of calls during my shift. Some of them are part of routine communications, such as gate chec...

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pytorch

Flow Models

June 21 2021

In this post, we will take a look at Flow models, which I’ve been obsessed with while reading papers like Glow-TTS and VITS. This post is heavily based on th...

Relative Positional Encoding

March 01 2021

In this post, we will take a look at relative positional encoding, as introduced in Shaw et al (2018) and refined by Huang et al (2018). This is a topic I me...

GPT from Scratch

February 15 2021

These days, I’m exploring the field of natural language generation, using auto-regressive models such as GPT-2. HuggingFace transformers offers a host of pre...

Attention is All You Need

January 20 2021

Today, we are finally going to take a look at transformers, the mother of most, if not all current state-of-the-art NLP models. Back in the day, RNNs used to...

Attention Mechanism

January 15 2021

Attention took the NLP community by storm a few years ago when it was first announced. I’ve personally heard about attention many times, but never had the ch...

(Attempt at) Knowledge Distillation

January 08 2021

For the past couple of months or so, I’ve been spending time looking into transformers and BERT. Transformers are state of the art NLP models that are now re...

Fast Gradient Sign Method

January 05 2021

In today’s post, we will take a look at adversarial attacks. Adversarial attacks have become an active field of research in the deep learning community, for ...

Better seq2seq

December 27 2020

In the previous post, we took a look at how to implement a basic sequence-to-sequence model in PyTorch. Today, we will be implementing a small improvement to...

Introduction to seq2seq models

December 20 2020

For a very long time, I’ve been fascinated by sequence-to-sequence models. Give the model a photo as input, it spits out a caption to go along with it; give ...

Neural Style Transfer

December 10 2020

In today’s post, we will take a look at neural style transfer, or NMT for short. NMT is something that I first came across about a year ago when reading Fran...

GAN in PyTorch

November 30 2020

In this blog post, we will be revisiting GANs, or general adversarial networks. This isn’t the first time we’ve seen GANs on this blog: we’ve implemented GAN...

InceptionNet in PyTorch

November 14 2020

In today’s post, we’ll take a look at the Inception model, otherwise known as GoogLeNet. I’ve actually written the code for this notebook in October 😱 but wa...

VGG PyTorch Implementation

November 01 2020

In today’s post, we will be taking a quick look at the VGG model and how to implement one using PyTorch. This is going to be a short post since the VGG archi...

PyTorch RNN from Scratch

October 25 2020

In this post, we’ll take a look at RNNs, or recurrent neural networks, and attempt to implement parts of it in scratch through PyTorch. Yes, it’s not entirel...

PyTorch, From Data to Modeling

October 20 2020

These past few weeks, I’ve been powering through PyTorch notebooks and tutorials, mostly because I enjoyed the PyTorch API so much and found so many of it us...

PyTorch Tensor Basics

October 10 2020

This is a very quick post in which I familiarize myself with basic tensor operations in PyTorch while also documenting and clarifying details that initially ...

A PyTorch Primer

September 14 2020

I’ve always been a fan of TensorFlow, specifically tf.keras, for its simplicity and ease of use in implementing algorithms and building models. Today, I deci...

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nlp

Relative Positional Encoding

March 01 2021

In this post, we will take a look at relative positional encoding, as introduced in Shaw et al (2018) and refined by Huang et al (2018). This is a topic I me...

BERT’s Common Sense, or Lack Thereof

February 18 2021

A few days ago, I came across a simple yet nonetheless interesting paper, titled “NumerSense: Probing Numerical Commonsense Knowledge of Pre-Trained Language...

GPT from Scratch

February 15 2021

These days, I’m exploring the field of natural language generation, using auto-regressive models such as GPT-2. HuggingFace transformers offers a host of pre...

NLI Models as Zero-Shot Classifiers

February 10 2021

In the previous post, we took a look at how to extract keywords from a block of text using transformer models like BERT. In that blog post, you might recall ...

Keyword Extraction with BERT

February 05 2021

I’ve been interested in blog post auto-tagging and classification for some time. Recently, I was able to fine-tune RoBERTa to develop a decent multi-label, m...

NLG with GPT-2

February 01 2021

When GPT-3 was released, people were amazed by its ability to generate coherent, natural-sounding text. In fact, it wasn’t just text; it could generate JavaS...

Attention is All You Need

January 20 2021

Today, we are finally going to take a look at transformers, the mother of most, if not all current state-of-the-art NLP models. Back in the day, RNNs used to...

Attention Mechanism

January 15 2021

Attention took the NLP community by storm a few years ago when it was first announced. I’ve personally heard about attention many times, but never had the ch...

Better seq2seq

December 27 2020

In the previous post, we took a look at how to implement a basic sequence-to-sequence model in PyTorch. Today, we will be implementing a small improvement to...

Introduction to seq2seq models

December 20 2020

For a very long time, I’ve been fascinated by sequence-to-sequence models. Give the model a photo as input, it spits out a caption to go along with it; give ...

BLEU from scratch

September 21 2020

Recently, I joined the Language, Information, and Learning at Yale lab, led by Professor Dragomir Radev. Although I’m still in what I would consider to be th...

Text Preprocessing with Blog Post Data

July 22 2020

In today’s post, we will finally start modeling the auto-tagger model that I wanted to build for more blog. As you may have noticed, every blog post is class...

Word2vec from Scratch

July 13 2020

In a previous post, we discussed how we can use tf-idf vectorization to encode documents into vectors. While probing more into this topic and geting a taste ...

Introduction to tf-idf

July 05 2020

Although I’ve been able to automate some portion of the blog workflow, there’s always been a challenging part that I wanted to further automate myself using ...

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from_scratch

Locality Sensitive Hashing

February 25 2021

These days, I’ve found myself absorbed in the world of memory-efficient transformer architectures. Transformer models require $O(n^2)$ runtime and memory due...

GPT from Scratch

February 15 2021

These days, I’m exploring the field of natural language generation, using auto-regressive models such as GPT-2. HuggingFace transformers offers a host of pre...

PyTorch RNN from Scratch

October 25 2020

In this post, we’ll take a look at RNNs, or recurrent neural networks, and attempt to implement parts of it in scratch through PyTorch. Yes, it’s not entirel...

BLEU from scratch

September 21 2020

Recently, I joined the Language, Information, and Learning at Yale lab, led by Professor Dragomir Radev. Although I’m still in what I would consider to be th...

Word2vec from Scratch

July 13 2020

In a previous post, we discussed how we can use tf-idf vectorization to encode documents into vectors. While probing more into this topic and geting a taste ...

Introduction to tf-idf

July 05 2020

Although I’ve been able to automate some portion of the blog workflow, there’s always been a challenging part that I wanted to further automate myself using ...

Gaussian Process Regression

July 02 2020

In this post, we will explore the Gaussian Process in the context of regression. This is a topic I meant to study for a long time, yet was never able to due ...

Building Neural Network From Scratch

February 05 2020

Welcome back to another episode of “From Scratch” series on this blog, where we explore various machine learning algorithms by hand-coding them from scratch....

Naive Bayes Model From Scratch

January 17 2020

Welcome to part three of the “from scratch” series where we implement machine learning models from the ground up. The model we will implement today, called t...

An Introduction to Markov Chain Monte Carlo

January 02 2020

Finally, here is the post that was promised ages ago: an introduction to Monte Carolo Markov Chains, or MCMC for short. It took a while for me to understand ...

Logistic Regression Model from Scratch

December 31 2019

This tutorial is a continuation of the “from scratch” series we started last time with the blog post demonstrating the implementation of a simple k-nearest n...

k-Nearest Neighbors Algorithm from Scratch

December 25 2019

These days, machine learning and deep neural networks are exploding in importance. These fields are so popular that, unless you’re a cave man, you have proba...

Recommendation Algorithm with SVD

December 09 2019

I’ve been using a music streaming service for the past few weeks, and it’s been a great experience so far. I usually listen to some smoothing new age piano o...

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analysis

Gamma and Zeta

July 28 2020

Maintaining momentum in writing and self-learning has admittedly been difficult these past few weeks since I’ve started my internship. Normally, I would writ...

Complex Fibonacci

July 10 2020

A few days ago, a video popped up in my YouTube suggestions. We all know how disturbingly powerful the YouTube recommendation algorithm is: more than 90 perc...

Revisiting Basel with Fourier

June 25 2020

In the last post, we revisited the Riemann Zeta function, which we had briefly introduced in another previous post on Euler’s take on the famous Basel proble...

Riemann Zeta and Prime Numbers

June 23 2020

The other day, I came across an interesting article by Chris Henson on the relationship between the Riemann Zeta function and prime numbers. After encounteri...

Newton-Raphson, Secant, and More

June 16 2020

Recently, I ran into an interesting video on YouTube on numerical methods (at this pont, I can’t help but wonder if YouTube can read my mind, but now I digre...

Understanding the Leibniz Rule

May 01 2020

Before I begin, I must say that this video by Brian Storey at Olin College is the most intuitive explanation of the Leibniz rule I have seen so far. Granted,...

Stirling Approximation

April 01 2020

It’s about time that we go back to the old themes again. When I first started this blog, I briefly dabbled in real analysis via Euler, with a particular focu...

Fourier Series

March 19 2020

Taylor series is used in countless areas of mathematics and sciences. It is a handy little tool in the mathematicians arsenal that allows us to decompose any...

Basel, Zeta, and some more Euler

November 26 2019

The more I continue my journey down the rabbit hole of mathematics, the more often I stumble across one name: Leonhard Euler. Nearly every concept that I lea...

The Magic of Euler’s Identity

November 17 2019

At a glance, Euler’s identity is a confusing, mind-boggling mishmash of numbers that somehow miraculously package themselves into a neat, simple form:

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update

Reflections and Expectations

December 27 2021

Last year, I wrote a blog post reflecting on the year 2020. Re-reading what I had written then was surprisingly insightful, particularly because I could see ...

Reboot

May 15 2021

It has been a while since I last posted on this blog. Admittedly, a lot has happened in my life: I have been discharged from the Republic of Korea Army, rece...

Reflections and Expectations

January 01 2021

2020 was unlike any other. The COVID pandemic fundamentally transformed our ways of life. Masks became a norm; classes were taught on Zoom; social distancing...

Django and Summer Internship

August 15 2020

For the past month and a half, I’ve been working as a backend developer for ReRent, a Yale SOM-based hospitality startup. Working alongside motivated, inspir...

Contributing Open Source

March 04 2020

Programming is difficult but fun. Or maybe it’s the other way around. Either way, any developer would know that external libraries are something that makes p...

First Date with Flask

February 28 2020

These past few days, I’ve been taking a hiatus from the spree of neural networks and machine learning to explore an entirely separate realm of technology: we...

Writing with Typora

January 26 2020

Disclaimer: I was not sponsored by the developers of Typora to write this post, although that would have been great.

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linear_algebra

The Gibbs Sampler

June 12 2020

In this post, we will explore Gibbs sampling, a Markov chain Monte Carlo algorithm used for sampling from probability distributions, somewhat similar to the ...

Principal Component Analysis

March 22 2020

Principal component analysis is one of those techniques that I’ve always heard about somewhere, but didn’t have a chance to really dive into. PCA would come ...

Building Neural Network From Scratch

February 05 2020

Welcome back to another episode of “From Scratch” series on this blog, where we explore various machine learning algorithms by hand-coding them from scratch....

Bayesian Linear Regression

January 20 2020

In today’s post, we will take a look at Bayesian linear regression. Both Bayes and linear regression should be familiar names, as we have dealt with these tw...

Recommendation Algorithm with SVD

December 09 2019

I’ve been using a music streaming service for the past few weeks, and it’s been a great experience so far. I usually listen to some smoothing new age piano o...

Linear Regression, in Two Ways

December 06 2019

If there is one thing I recall most succinctly from my high school chemistry class, it is how to use Excel to draw basic plots. In the eyes of a naive freshm...

Markov Chain and Chutes and Ladders

November 16 2019

In a previous post, we briefly explored the notion of Markov chains and their application to Google’s PageRank algorithm. Today, we will attempt to understan...

Understanding PageRank

November 13 2019

Google is the most popular search engine in the world. It is so popular that the word “Google” has been added to the Oxford English Dictionary as a proper ve...

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probability_distribution

Beta, Bayes, and Multi-armed Bandits

August 28 2020

Recently, I fortuitously came across an interesting blog post on the multi-armed bandit problem, or MAB for short. I say fortuitous because the contents of t...

Stirling Approximation

April 01 2020

It’s about time that we go back to the old themes again. When I first started this blog, I briefly dabbled in real analysis via Euler, with a particular focu...

The Exponential Family

January 22 2020

Normal, binomial, exponential, gamma, beta, poisson… These are just some of the many probability distributions that show up on just about any statistics text...

Moments in Statistics

December 16 2019

The word “moment” has many meanings. Most commonly, it connotes a slice of time. In the realm of physics, moment refers to the rotational tendency of some ob...

Dissecting the Gaussian Distribution

December 12 2019

If there is one thing that the field of statistics wouldn’t be complete without, it’s probably normal distributions, otherwise referred to as “the bell curve...

A sneak peek at Bayesian Inference

November 30 2019

So far on this blog, we have looked the mathematics behind distributions, most notably binomial, Poisson, and Gamma, with a little bit of exponential. These ...

0.5!: Gamma Function, Distribution, and More

November 22 2019

In a previous post, we looked at the Poisson distribution as a way of modeling the probability of some event’s occurrence within a specified time frame. Spec...

How lucky was I on my shift?

November 20 2019

At the Yongsan Provost Marshall Office, I receive a wide variety of calls during my shift. Some of them are part of routine communications, such as gate chec...

Back to Top ↑

machine_learning

Gaussian Mixture Models

August 01 2020

We’ve discussed Gaussians a few times on this blog. In particular, recently we explored Gaussian process regression, which is personally a post I really enjo...

Gaussian Process Regression

July 02 2020

In this post, we will explore the Gaussian Process in the context of regression. This is a topic I meant to study for a long time, yet was never able to due ...

Scikit-learn Pipelines with Titanic

May 30 2020

In today’s post, we will explore ways to build machine learning pipelines with Scikit-learn. A pipeline might sound like a big word, but it’s just a way of c...

Natural Gradient and Fisher

May 27 2020

In a previous post, we took a look at Fisher’s information matrix. Today, we will be taking a break from the R frenzy and continue our exploration of this to...

Naive Bayes Model From Scratch

January 17 2020

Welcome to part three of the “from scratch” series where we implement machine learning models from the ground up. The model we will implement today, called t...

First Neural Network with Keras

January 15 2020

Lately, I have been on a DataCamp spree after unlocking a two-month free unlimited trial through Microsoft’s Visual Studio Dev Essentials program. If you hav...

Logistic Regression Model from Scratch

December 31 2019

This tutorial is a continuation of the “from scratch” series we started last time with the blog post demonstrating the implementation of a simple k-nearest n...

k-Nearest Neighbors Algorithm from Scratch

December 25 2019

These days, machine learning and deep neural networks are exploding in importance. These fields are so popular that, unless you’re a cave man, you have proba...

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tensorflow

My First GAN

February 25 2020

Generative models are fascinating. It is no wonder that GANs, or General Adversarial Networks, are considered by many to be where future lies for deep learni...

A Step Up with Variational Autoencoders

February 22 2020

In a previous post, we took a look at autoencoders, a type of neural network that receives some data as input, encodes them into a latent representation, and...

So What are Autoencoders?

February 18 2020

In today’s post, we will take yet another look at an interesting application of a neural network: autoencoders. There are many types of autoencoders, but the...

A Simple Autocomplete Model

February 10 2020

You might remember back in the old days when autocomplete was just terrible. The suggestions provided by autocomplete would be useless if not downright stupi...

Convolutional Neural Network with Keras

February 01 2020

Recently, a friend recommended me a book, Deep Learning with Python by Francois Chollet. As an eager learner just starting to fiddle with the Keras API, I de...

First Neural Network with Keras

January 15 2020

Lately, I have been on a DataCamp spree after unlocking a two-month free unlimited trial through Microsoft’s Visual Studio Dev Essentials program. If you hav...

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jupyter

Blog Workflow Cleanup

May 23 2020

These past few days, I’ve been writing posts on R while reading Hadley Wickham’s R for Data Science. R is no Python, but I’m definitely starting to see what ...

SQL Basics with Pandas

May 19 2020

Recently, I was compelled by my own curiosity to study SQL, a language I have heard about quite a lot but never had a chance to study. At first, SQL sounded ...

A Short R Tutorial

January 09 2020

This is an experimental jupyter notebook written using IRkernel. The purpose of this notebook is threefolds: first, to document my progress with self-learnin...

Conda Virtual Environments with Jupyter

January 07 2020

As a novice who just started learning Python just three months ago, I was clueless about what virtual environments were. All I knew was that Anaconda was pur...

Using Jupyter Notebook with Jekyll

November 30 2019

In the last post, I tested out the functionality of Jupyter Notebook, a platform that I am just starting to get acquainted with. I’m pleased with how that ex...

Experimenting with Jupyter Notebook

November 29 2019

So far on this blog, all posts were written using Markdown. Markdown is very easy and learnable even for novices like me, but an issue I had was the inconven...

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r

Blog Workflow Cleanup

May 23 2020

These past few days, I’ve been writing posts on R while reading Hadley Wickham’s R for Data Science. R is no Python, but I’m definitely starting to see what ...

R Tutorial (4)

May 22 2020

In this post, we will continue our journey down the R road to take a deeper dive into data frames. R is great for data analysis and wranging when it comes to...

R Tutorial (3)

May 10 2020

A few days ago, I saw a friend who posted an Instagram story looking for partners to study R with. I jumped at the opportunity without hesitation—based on my...

R Tutorial (2)

April 25 2020

In this post, we will continue our journey with the R programming language. In the last post, we explored some basic plotting functions and how to use them t...

R Tutorial (1)

April 16 2020

It’s been a while since we last took a look at the R programming language. While I don’t see R becoming my main programming language (I’ll always be a Python...

A Short R Tutorial

January 09 2020

This is an experimental jupyter notebook written using IRkernel. The purpose of this notebook is threefolds: first, to document my progress with self-learnin...

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simulation

Monte Carlo Coin Toss

November 23 2020

While mindlessly browsing through Math Stack Exchange, I stumbled across an interesting classic:

Beta, Bayes, and Multi-armed Bandits

August 28 2020

Recently, I fortuitously came across an interesting blog post on the multi-armed bandit problem, or MAB for short. I say fortuitous because the contents of t...

Wonders of Monte Carlo

December 03 2019

I have been putting off with blog postsings lately, largely because I was preoccupied with learning new languages I decided to pick up out of whim. Although ...

Markov Chain and Chutes and Ladders

November 16 2019

In a previous post, we briefly explored the notion of Markov chains and their application to Google’s PageRank algorithm. Today, we will attempt to understan...

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bayesian

Beta, Bayes, and Multi-armed Bandits

August 28 2020

Recently, I fortuitously came across an interesting blog post on the multi-armed bandit problem, or MAB for short. I say fortuitous because the contents of t...

Bayesian Linear Regression

January 20 2020

In today’s post, we will take a look at Bayesian linear regression. Both Bayes and linear regression should be familiar names, as we have dealt with these tw...

Naive Bayes Model From Scratch

January 17 2020

Welcome to part three of the “from scratch” series where we implement machine learning models from the ground up. The model we will implement today, called t...

An Introduction to Markov Chain Monte Carlo

January 02 2020

Finally, here is the post that was promised ages ago: an introduction to Monte Carolo Markov Chains, or MCMC for short. It took a while for me to understand ...

A sneak peek at Bayesian Inference

November 30 2019

So far on this blog, we have looked the mathematics behind distributions, most notably binomial, Poisson, and Gamma, with a little bit of exponential. These ...

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monte_carlo

Rejection Sampling

January 25 2021

In today’s post, we will take a break from deep learning and turn our attention to the topic of rejection sampling. We’ve discussed the topic of sampling som...

Monte Carlo Coin Toss

November 23 2020

While mindlessly browsing through Math Stack Exchange, I stumbled across an interesting classic:

The Gibbs Sampler

June 12 2020

In this post, we will explore Gibbs sampling, a Markov chain Monte Carlo algorithm used for sampling from probability distributions, somewhat similar to the ...

An Introduction to Markov Chain Monte Carlo

January 02 2020

Finally, here is the post that was promised ages ago: an introduction to Monte Carolo Markov Chains, or MCMC for short. It took a while for me to understand ...

Wonders of Monte Carlo

December 03 2019

I have been putting off with blog postsings lately, largely because I was preoccupied with learning new languages I decided to pick up out of whim. Although ...

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markov_chain

The Gibbs Sampler

June 12 2020

In this post, we will explore Gibbs sampling, a Markov chain Monte Carlo algorithm used for sampling from probability distributions, somewhat similar to the ...

An Introduction to Markov Chain Monte Carlo

January 02 2020

Finally, here is the post that was promised ages ago: an introduction to Monte Carolo Markov Chains, or MCMC for short. It took a while for me to understand ...

Markov Chain and Chutes and Ladders

November 16 2019

In a previous post, we briefly explored the notion of Markov chains and their application to Google’s PageRank algorithm. Today, we will attempt to understan...

Understanding PageRank

November 13 2019

Google is the most popular search engine in the world. It is so popular that the word “Google” has been added to the Oxford English Dictionary as a proper ve...

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regression

Gaussian Process Regression

July 02 2020

In this post, we will explore the Gaussian Process in the context of regression. This is a topic I meant to study for a long time, yet was never able to due ...

Bayesian Linear Regression

January 20 2020

In today’s post, we will take a look at Bayesian linear regression. Both Bayes and linear regression should be familiar names, as we have dealt with these tw...

Logistic Regression Model from Scratch

December 31 2019

This tutorial is a continuation of the “from scratch” series we started last time with the blog post demonstrating the implementation of a simple k-nearest n...

Linear Regression, in Two Ways

December 06 2019

If there is one thing I recall most succinctly from my high school chemistry class, it is how to use Excel to draw basic plots. In the eyes of a naive freshm...

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euler

Riemann Zeta and Prime Numbers

June 23 2020

The other day, I came across an interesting article by Chris Henson on the relationship between the Riemann Zeta function and prime numbers. After encounteri...

Basel, Zeta, and some more Euler

November 26 2019

The more I continue my journey down the rabbit hole of mathematics, the more often I stumble across one name: Leonhard Euler. Nearly every concept that I lea...

The Magic of Euler’s Identity

November 17 2019

At a glance, Euler’s identity is a confusing, mind-boggling mishmash of numbers that somehow miraculously package themselves into a neat, simple form:

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algorithms

Locality Sensitive Hashing

February 25 2021

These days, I’ve found myself absorbed in the world of memory-efficient transformer architectures. Transformer models require $O(n^2)$ runtime and memory due...

On BFS and DFS

June 20 2020

In this post, we will be taking a look at a very simple yet popular search algorithm, namely breadth-first search and depth-first search methods. To give you...

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data_viz

Plotting Prime Numbers

January 10 2021

Today’s article was inspired by a question that came up on a Korean mathematics Facebook group I’m part of. The gist of the question could probably be transl...

Data Viz Basics with Python

September 28 2020

This post is based on this article on Medium, titled “Matplotlib + Seaborn + Pandas: An Ideal Amalgamation for Statistical Data Visualization.” This article ...

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Apple

16-inch MacBook Pro Announced

November 14 2019

Apple officially announced the new 16-inch MacBook Pro. This product has been a long awaited release for many tech enthusiasts particularly given the negativ...

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military

How lucky was I on my shift?

November 20 2019

At the Yongsan Provost Marshall Office, I receive a wide variety of calls during my shift. Some of them are part of routine communications, such as gate chec...

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c

Learning C

May 05 2020

So I’ve been spending some time this past week or so picking up a new language: C. C is considered by many to be one of the most basic and fundamental of all...

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sql

SQL Basics with Pandas

May 19 2020

Recently, I was compelled by my own curiosity to study SQL, a language I have heard about quite a lot but never had a chance to study. At first, SQL sounded ...

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spark

Introduction to PySpark

June 05 2020

I’ve stumbled across the word “Apache Spark” on the internet so many times, yet I never had the chance to really get to know what it was. For one thing, it s...

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numerical_methods

Newton-Raphson, Secant, and More

June 16 2020

Recently, I ran into an interesting video on YouTube on numerical methods (at this pont, I can’t help but wonder if YouTube can read my mind, but now I digre...

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docker

Docker Blitz

July 17 2020

Docker was one of these things that I always wanted to learn, but never got into. Part of the reason was that it seemed distant and even somewhat unnecessary...

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analyssis

Plotting Prime Numbers

January 10 2021

Today’s article was inspired by a question that came up on a Korean mathematics Facebook group I’m part of. The gist of the question could probably be transl...

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