Daniel Drentin

Drentin’s Findings

I am a software engineer from Melbourne, Australia, currently concentrating on AWS-based solutions
Using a Image Classification Model to detect the first 5 Japanese Hiragana characters

ML with a AWS DeepLens: Is it あ? Yes it is!

Starting last week I felt like I was ready to jump into attempting to work off the knowledge I had built up doing the examples previously and attempt the main goal; to create a model that was capable of recognising some of the Japanese Hiragana character set. Problem - Input Data! My initial problem that I had been playing with was the idea of a training data set - where would I get a good set of training data from?...

September 24, 2021 · 4 min
When the hexagonality of your hexagons are in question

ML with a AWS DeepLens: Compost, Landfill or Recycling..?

What we all came here for… SageMaker! I was excited to get stuck into the Advanced recipe - Build a custom ML model to sort trash as this started getting into the parts I wanted to know more about; how to get a basic model trained in SageMaker and then deploy it to the DeepLens device. Step 1 - Train! Luckily in this example they include a number of sample images, quite a decent set really with over 500 images in total, seperate into Compost, Landfill and Recycling....

September 15, 2021 · 3 min
Asking the hard(er) questions!

ML with a AWS DeepLens: First figure out how to do... anything!

Beginnings start here! Hello all and welcome to my first article around my attempts to create an Amazon SageMaker-based solution, focusing on image detection. I am participating in an initiative as part of my company’s AWS Community of Practice. The idea is inspired loosely by A Cloud Guru’s How to Build a Netflix Style Recommendation Engine with Amazon SageMaker Challenge and I have got my hands on a AWS DeepLens so I’m going to see what I can do with both of these to the best of my ability!...

September 13, 2021 · 6 min