DEV Community: Andrew D'Armond

Good Boy/Girl Forever!

Andrew D'Armond — Thu, 08 Oct 2020 00:10:56 +0000

Love Dogs? Check it!

I was able to take my deep learning to the next level, with this handsome guy here. His name is Ranger, but sometimes I wonder what Rangers mix is as he is a good ole craigslist dog. As many dogs are not a pure breed but mixes and no one has time to do vet blood work to tell what they are, so I thought my next project would be fun for all ages to see what furr ball you could have. I used the resnet34 image classification architecture to train the model to get an error rate of 7%! An industry-standard in today's image classification architecture. The images were trained on bing search images of some of the top breeds listed on the American Kennel Club. Bing has an easy to gain access to API that allows you to easily gather up to 150 images per parameter in this case dog breed. I was able to gather 5000 images to train my model on in the process. You can find the app here in my first link and see if your good boy/girl can be classified.

Tips:
Full body shot
Good lighting
limited/no obstructions

Because the images are trained are perfectly taken pictures found on search engines such as bing these tips are very important otherwise you will get your dog classified as a cat! Not really, but still reasons being that color, light, and visibility of the puppy, in general, are the main source to recognize the image.

Also, the ability to launch the app will take a min as the file is made into a docker file by binder and has to be recreated each time.

App/ Github Repo
https://github.com/andrewdarmond/PuppyPalooza

Fastai:
https://www.fast.ai/

Binder:
https://mybinder.org/

Docker:
https://www.docker.com/play-with-docker

Who's a good pup!

Naïve Bayes Deep Learning

Andrew D'Armond — Wed, 23 Sep 2020 03:54:26 +0000

Can a machine identify a bee as a honey bee or a bumblebee? These bees have different behaviors and appearances, but given the variety of backgrounds, positions, and image resolutions, it can be a challenge for machines to tell them apart.

Find out here at https://www.andrewdarmond.com/post/naive-bees-deep-learning/

Apple vs PC

Andrew D'Armond — Tue, 22 Sep 2020 22:52:31 +0000

Introduction:
In this data science project, I have conducted a machine learing techniques on categorical data or commonly known as Unsupervised Learning. The code has been conducted in python and the results have found some very high quality results for Apple users.I have posted the code in the tab above labeled code and here. The data has been collected by university students in the hopes of better understanding purchasing decisions in what is the key demographic of most businesses, ages ~21-35. It’s best to not dive deep in some of the insights termonalogy as the “Persona’s” mentioned below are standard practice when aliasing data in order to better give feedback to non techinical leaders. Here below are the main insights from the data. Happy Shopping!

Check out my full project here: https://www.andrewdarmond.com/project/laptop/

Travel Shiny App

Andrew D'Armond — Tue, 22 Sep 2020 22:51:00 +0000

Reason for project
Today, many people enjoy the life of a traveler. Traveling presents an opportunity to enjoy life like you never will again. The ability to enjoy things without the idea of consequence is one that many aspire for as can’t escape their boring day. The world is still big even though today even the advancements of social media bring our most wonderous ideas of adventure at our fingertips. Still, nothing escapes the thrill of embarking on a new journey that you will talk about up until your next destination. Many of my constituents travel almost as a job and their knowledge in destination ideas through a survey that was conducted between us all can provide unmeasured insights into your next travel plans. The ability to scale the technology in this project is endless as the tech feeds through an open-source network or can be found on a simple .docx.

Check out my full project here: https://www.andrewdarmond.com/project/travel/

NCAA Basketball Analysis

Andrew D'Armond — Tue, 22 Sep 2020 22:47:07 +0000

Completed an analysis using data from the NCAA basketball datasets provided on Kaggle.

Introduction
This analysis is using data from NCAA men and women's basketball over the last 30 years. The objective is to find out how the sport shapes up using one of the most widely known sports analytical terms in Pythagorean Expectation. The report will be in a series of Markdown files and will feature other sports including the NBA, NFL, NHL, and EPL.

Check out the full article here: https://www.andrewdarmond.com/post/ncaa/

Website

Andrew D'Armond — Tue, 22 Sep 2020 22:41:01 +0000

Hello Friends!

Who is Andrew D’Armond?
A boy born in Alexandria, VA 🍼
A man raised in Houston, TX 🏡
A sports fanatic. 🏈
A data science enthusiast 💻
A music connoisseur 🎧
A world explorer 🌐

My Goals:
GET A JOB 😅

To find a job that allows me to practice, enhance, and sharpen my data science skills and programming knowledge. I am free to negotiate on salary, location, title, etc. I just want the chance to make this more than a hobby. I know that many of you are in the same position, so let’s stand up together and work for each other.

What you can do??
Please share my website with all social media.
It makes a difference because just one share creates an exponential growth to a whole network of connections. See “Neural Networks” postings online and you will see the ability in the science.

Leave Feedback
The key to anything is not to be stubborn and be accepting of new things and ideas. I must get any notes regardless of the length, detail, scope, etc. I welcome it!

THANK YOU!

Check it out here: https://www.andrewdarmond.com/