Once you have a number of models logged, you have way more dimensions to examine than can be looked at all at once. One powerful visualization tool we've discovered is the parallel coordinates chart. Here each line is an individual experiment and each column is an input hyperparameter or an output metric. I've highlighted the top accuracy runs and it shows quite clearly that across all of my experiments that I've selected, high accuracy comes from low dropout values. Aggregate metrics are good, but it is essential to look at specific examples.
At first glance, building and deploying machine learning models looks a lot like writing code. Tracking experiments in an organized way helps with all of these core issues. Weights and Biases (wandb) is a simple tool that helps individuals to track their experiments -- I talked to several machine learning leaders of different size teams about how they use wandb to track their experiments. The essential unit of progress in an ML project is an experiment, so most people track what they're doing somehow -- generally I see practitioners start with a spreadsheet or a text file to keep track of what they're doing. Spreadsheets and docs are incredibly flexible -- what's wrong with this approach?
If you are training models in an automated environment where it's inconvenient to run shell commands, such as Google's CloudML, you should look at the documentation on Running in Automated Environments. Sign up for a free account in your shell or go to our sign up page. Add a few lines to your script to log hyperparameters and metrics. Weights and Biases is framework agnostic, but if you are using a common ML framework, you may find framework-specific examples even easier for getting started. We've built framework-specific hooks to simplify the integration for Keras, TensorFlow, PyTorch, Fast.ai,
Now that Leila is finished with own her conference http://difinity.co.nz/ we get her to walk through two of her favorite topics: R Script and Power BI specifically: Power BI visualization is a holistic visualization tool. You can draw most of the popular charts with it. However, there is always some needs for specific charts that may not be available in Power BI standard visualizations of the marketplace. There is a possibility to extend the visualization capabilities using R language. There are two ways to use R to extend the visualization possibilities.
This is a small tutorial on how to estimate prices of houses in Pharo using linear regression model from PolyMath. We will then visualize the data points together with the regression line using the new charting capabilities of Roassal3. The main purpose of this blog post is to demonstrate the new charting functionality of Roassal3 that were introduced yesterday. The visualization that we will build is not very pretty, but it will give you a taste of the amazing things that we will be able to do in the near future. Pharo is a pure object-oriented programming language and a powerful environment, focused on simplicity and immediate feedback (think IDE and OS rolled into one).