M2M Day 200: This is why Google can give away its algorithms for free
This post is part of Month to Master, a 12-month accelerated learning project. For May, my goal is to build the software part of a self-driving car.
Yesterday, I started training the self-driving car model based on Udacity’s large dataset.
The Loss value (the measure indirectly proportional to the model’s accuracy) started at 6.14783.
Many hours later, the model finished training, reducing the Loss to only 0.000377398.
Here’s the plot of Loss over time:
This reduction in Loss is quite striking.
Even more strikingly though is the difference in Loss when using 5,000 datapoints (as I did a few days ago), with a Loss of ~1.00, and when using Udacity’s 33,000 datapoints, with a Loss of around 0.000377398.
In other words, by increasing the size of the dataset by a factor of ~7, the loss was reduced by a factor of ~2500. Clearly, this isn’t a linear relationship: With a little bit more data, the model becomes ridiculously better.
And this is why Google can afford to give away all/most of its machine learning algorithms and libraries via TensorFlow: Quantity of data is the differentiator, and Google has the most (from search, email, photos, YouTube videos, etc.)
In fact, it’s in Google’s best interest to opensource its algorithms, allowing a larger community of developers to improve the algorithms more quickly. Then, Google can take these improved algorithms, feed them its proprietary data, and have the best machine learning models by a significant margin.
Today really helped me appreciate the value of data as a competitive advantage.
Talking about data… I’ve reached a milestone in the creation of my own dataset: Today marks the 200th day in a row that I’ve written a blog post as part of my Month to Master project.
My dataset of daily entries now totals around 85,000 words. Maybe, once I’m done with all 365 posts, I’ll figure out something interesting to do with this dataset…
Read the next post. Read the previous post.
