The original Chapter 10 lab made use of keras, an R package for deep learning that relies on Python. Getting keras to work on your computer can be a bit of a challenge. Installation instructions are available here. RStudio has recently released a new R package for deep learning, called torch, that does not require a Python installation. Daniel Falbel and Sigrid Keydana, two of the torch developers, translated our keras version of the Chapter 10 lab to torch.

This is a project that is organized by Datatalks.Club. In this competition, one has to train a deep learning model in tensorflow or pytorch to classify kitchenware items. I used tensorflow and keras for this task. As an image classification model, when given the image of one of the above-listed kitchenware items, the model will output probailities for each of the six classes. The highest probability serves as the model's final classification.

Existing experiment trackers come with a high setup cost. To get one working, you usually have to spin up a database and run a web application. After trying multiple options, I thought that using Jupyter notebooks could be an excellent choice to store experiment results and retrieve them for comparison. This post explains how I use .ipynb Machine Learning is a highly iterative process: you don't know in advance what combination of model, features, and hyperparameters will work best, so you need to make slight tweaks and evaluate performance.