In this blog post we’ll talk about how we used deep learning to create a Sudoku solver. The link to our Github repo can be found here. In the repository there is also a helpful README file that you guys can check out.

In this blog post, we will be developing a text classifier using TensorFlow that can detect misinformative news articles. We will be using a dataset from kaggle containing the titles and complete text from labeled ‘fake’ and ‘not fake’ news articles.

In this blog post, I’m going to be training various neural networks to perform image classification. More specifically, we will be training these networks to properly classify images of dogs and cats.

First, lets load our data

Here we will read in our data and use the image_dataset_from_directory to load it into TensorFlow objects.

In this blog post, I’m going to be making a super useful web scraper using scrapy. We will be scraping the IMDB website to find movies and TV shows starring actors from our favorite film/TV shows. In my case, I chose Star Trek: Deep Space Nine. Here’s a link to my project repository.

In this blog post, we will be working with SQL querying and the python plotly package to generate insightful visualizations of the NOAA Climate Data.

Step 1. Creating a Database

The first step to querying with SQL is to create a database containing all the relevant tables. Here, we will be working with these tables (given as csv files):

• temperatures: (giving temperatures at various weather stations all over the world for each month of each year )

• countries: (a reference table providing the country code used for station identifiers in each country)

• stations: (giving the name and location in latitude and longitude of each weather station)

Step 1. Read the data into Python using pandas

The pandas Python package allows us to read the data set, and facilitates data manipulation. After importing the pandas package, we read the url containing our dataset into pandas with the read_csv function.

import pandas as pd
url = "https://raw.githubusercontent.com/PhilChodrow/PIC16B/master/datasets/palmer_penguins.csv"
penguins = pd.read_csv(url)