In this blog post, I’ll write a reflection on the class project I’ve done with team YongNRich, as well as my project contribution part.

In this blog post, I’ll create a machine learning model for classifying fake news using Tensorflow supported by python. By using the python dataset, we’ll use the general format of kaggle dataset created by python.

In this blog post, I’ll write a tutorial on a simple version of the spectral clustering algorithm for clustering data points. Each of the below parts are necessary specific tasks for creating the spectral clustering.

This blog post includes some graphics related to global climate change. Data sourced from the NOAA climate data.

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Data visualization of the Palmer Penguins data set clustered by species, mainly via the seaborn library. Two general relations are considered: Culmen Length vs Culmen Depth, and Flipper Length vs Body Mass.

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

The purpose of this homework assignment is to get you set up with the software tools we’ll use in PIC16B, including Anaconda, git + GitHub, and Jekyll.

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.