In this blog post we will be examining the process of Natural Language Processing using tensorflow through a case study of fake news. These articles were collected in the research paper:

We begin our blog post by using the template code provided by Professor Chodrow. This code initializes the datasets we will use for training and validation of our model. This code block also uses the tf.data module to speed up the loading of images.

Here we’ll analyze the basics of spectral clustering and try to derive it ourselves. I will explain my reasoning behind the functions used and how they operate. The source material notebook is copied from Professor Chodrow’s source.

This blog post will review my creation of a dynamic web application using Flask. You can view my code on Github here. My repo began as a fork of John Zhang’s repo flask-interactions. This gave me a good basis on which to build my database.

This is a project for PIC16B at UCLA where we write a webscraper to collect a list of TV shows which share at least one actor with one of our favorite shows. In this blog post I will be using a recent favorite of mine, Demon Slayer. We then perform a light analysis of these movies/TV shows. My repo is here if you wish to follow along. One future improvement I plan to incorporate is a bash script which takes a string argument of either a link to content on imdb or a name and then runs the scraper and analysis to automate some of the hard-coded variables in this project.

Interesting Graphs with Plotly

Covering the basics of visualization in Matplotlib

Woo Hoo this post is so cool!

In this post, we’ll get set up with Jekyll. Jekyll is a static site converter, which you can use to turn plaintext documents into attractive webpages.

Fortunately, it’s pretty easy to embed interactive HTML figures produced via Plotly on your blog. Just use plotly.io.write_html() to save your figure. Then, copy the resulting HTML file to the _includes directory of your blog. Finally, place the code

It is possible to construct, maintain, and update your blog fully from GitHub. In this case, it is not necessary to download your blog’s files or modify them on your computer. However, when constructing complex posts involving code and figures, local editing can be more comfortable. Additionally, since GitHub Pages usually takes a few minutes to publish all your changes, modifying your blog locally allows you to more quickly see the results of your changes, including errors when they arise. In this post, I’ll show how to manage your blog locally.

In this post, I’ll show how to create a helpful histogram of some synthetic data.

In this post, we’ll see some examples of how to create technical posts that include Python code, explanatory text, and notes about your learnings. We’ll go over the primary methods that you’ll use to embed content in your posts.