Graph Theory I
In this post, I cover the basics of graph theory, including graph representations and simple graph search methods. In writing this post, I referenced my own ...
Posts by Category
study
Recursion I
Tower of Hanoi
SVD, PCA
In this post, I wrote about the singular value decomposition and the principal component analysis.
Distributions II
In this post, I discuss the gamma, chi-squared, and exponential distributions.
Backprop with LSTM
Background
Recurrent Networks
This post covers the definition, principles, and basic workings of recurrent neural nets. The materials referenced in this post include the obvious ones, suc...
PyTorch Basics
This post covers my attempts at learning PyTorch–a framework that I had long intended to use, but never exactly had time to master in depth. Whereas experime...
Topic Identification with Tf-Idf
In this post, I will go over my first attempt at topic identification–the elementary stepping-stone to more advanced natural language processing tasks.
CNN with TensorFlow
In this post, I will cover my first attempt at learning about convolutional neural networks.
EDA with R
In this post, I will go over the what is perhaps the most important step in using R to analyze data–tidying. In fact, provided that many R functions work bes...
Classifying Movie Reviews: Keras
In this blog post, I will continue on from the last post in experimenting with Keras, mainly referencing François Chollet’s text, Machine Learning with Pytho...
MNIST with Keras
In this post, I will discuss my first attempt at creating a neural network with Keras, in which I will classify hand-written digits using the MNIST dataset.
Dive into Distributions
In this post, let’s dive into distributions.
Data Transformation with R
This post is a continuation of my recent blog posts on learning R. This post, along with the others, references Hadley Wickham and Garret Grolemund’s R for D...
Visualization with R
In this post, I will continue on from the previous post on learning R, but will focus primarily on data visualization. As stated before, the contents of this...
First Attempt at R
In this post, I will go over what I learned during my first attempt at R.
Continued: DataFrame & Matplotlib
Identifying Values: Columns, Rows
Quick Review of DataFrame & Matplotlib
In this post, I created my own reference guide to dataframe and matplotlib.
Linear Mapping & Basis Change
In this post, I will go over some basic linear algebra used in machine learning.
Decision Tree
In this post, I will explain another popular supervised machine learning tool called a decision tree classifier.
Web scraping: a tutorial
In this post, I will go over what I learned while exploring some features of Beautiful Soup and data extraction using web-based API.
Mathematical Statistics
In this post, I will discuss some of the basic statistical underpinnings of machine learning.
Default or No Default?
In this post, I will briefly go over an example of a Scikit-learn-based implementation of a support vector machine–a popular example of a supervised learning...
K-Means Clustering: Height/Weight
Previously, I posted about several examples of supervised learning algorithms, such as k-nearest neighbor. In this post, we will look at k-means clustering, ...
Logistic Regression
In this post, I will go over the “hello world” of machine learning–logistic regression. This post references Andrew Ng’s 2020 lecture on machine learning, av...
first-post
First Post
Life isn’t about finding yourself. Life is about creating yourself. – George Bernard Shaw