A brief guide on how to deploy deep learning model in browser using tensorflow.js.In this post, a mobileNet model was trained to predict BMI, Age and Gender. The model takes input (either from webcam or uploaded files) to make prediction from browser. This deployment has a obvious advantage of reduced uploading traffic compared to RESTful API approach.
An example of deploying deep learning model in browser using tensorflow.js
Presenting an API is never going to be attractive. In this post, I documented my approach of developing a web page on top of existing API using FastAPI + Vue.js technology stack.
Want to include a data summary as quick reference in your Jupyter notebooks ? I used to have summarytools package in R to do this. I miss that one when I’m doing python projects. So I developed a similar python function with some additional widgets. Please check out this post if you are interested.
sharing a range of packages and tips to build a reproduciable analysis presentation in R markdown