Dim Sum Classifier – from Data to App part 2

5 min. read Picture Credits here In the previous post, we see how we can acquire data, process, clean and train an Image Classifier to identify some yummy dim sums. In this post, we shall look at completing the loop by developing the web app using starlette (a framework similar to that of flask but supports asynchronous IO), […]

Dim Sum Classifier – from Data to App part 1

7 min. read Picture Credits here In a typically machine learning lifecycle, we will need to acquire data, process data, train and validate/test models and finally deploy the trained models in applications/services. In this first part of two post, inspired by fast.ai 2019 lesson 2, we shall build a Dim Sum (a Cantonese bite-size style of cuisine with […]

Rock, paper, scissors – vision transfer learning with fast.ai

7 min. read Picture Credits: Wikipedia In the previous post, we used the Rock, Paper Scissors notebook that trained a custom image classification model from scratch. While the notebook is demonstrates building custom layers, for such a task, we can also leverage on Transfer Learning using models trained on similar image classification tasks that can often reduce time […]

Getting session information in Python

< 1 min. read We’ve gone through how to get session information in R previously, so how do we do the same for Python? It seems that there is no single convenient function available so here’s one approach. To get the system information, you can utilize the commonly used IPython package: import IPython IPython.sys_info() To find out packages that […]

A virtual environment for data science

< 1 min. read I wanted to conveniently use data science tools without the hassle of installing the required languages and packages, while benefiting from the strengths of the Linux command line tools. There is a pre-packaged VM called the Data Science Toolbox that fills this need. It comes with R and Python installed, along with the respective popular […]