I took a bunch of deep learning in college, but very little of it was 'applied'—we didn't learn how to build or truly deploy applications! Practical Deep Learning for Coders is largely designed to bridge that gap. It's a self-paced online course that gets you to apply what you learn at each step.

I've built a bunch of projects for it, including:

• a HuggingFace applet (pictured above) to classify houseplants
• this NLP model to predict the sentiment of various Tweets about COVID-19
• a tabular model to estimate how many bike rentals a shop would have based on weather, day of the week, etc
• a random forest to predict Census income based on demographic features

A decision tree and the results of a random forest to predict Census income

As I complete each lesson, I've also been blogging about it; they strongly encourage you to do this to retain and actually engage with the material, and I've gotten a lot out of it! You can read all of my writeups below:

0. Lesson #0 (1/18/24)
1. Lesson #1 - Getting Started (1/18/24)
2. Lesson #2 - Deployment (1/19/24)
3. Lesson #3 - Neural net foundations (7/24/24)
4. Lesson #4 - Natural language processing (7/25/24)
5. Lesson #5 - From-scratch model (7/27/24)
6. Lesson #6 - Random Forests (8/1/24)