Simon Willison’s Weblog

Items tagged machinelearning in 2019

Filters: Year: 2019 × machinelearning ×


Machine Learning on Mobile and at the Edge: 2019 industry year-in-review (via) This is a fantastic detailed overview of advances made in the field of machine learning on the edge (primarily on mobile devices) over 2019. I’m really excited about this trend: I love the improved privacy implications of running models on my phone without uploading data to a server, and it’s great to see techniques like Federated Learning (from Google Labs) which enable devices to privately train models in a distributed way without having to upload their training data. # 30th December 2019, 10:17 pm

We don’t like limits on discrimination and lending, so we’re gonna use machine learning, which is a form of money laundering for bias, a way to blame mathematical algorithms for desires to simply avoid rules that everybody else has to play by in this industry.

Maciej Ceglowski # 8th May 2019, 11:11 pm

Exploring Neural Networks with Activation Atlases. Another promising attempt at visualizing what’s going on inside a neural network. # 19th April 2019, 2:24 am

Practical Deep Learning for Coders 2019 (via) The deep learning evening course I took a few months ago has now been shared online in full, and it’s outstanding. “After the first lesson you’ll be able to train a state-of-the-art image classification model on your own data”—can confirm: after just the first lesson I built a bobcat v.s. cougar classifier using photos from iNaturalist. The biggest thing I learned from the course is how powerful transfer learning is. I used to think you needed a huge amount of data to get good results from deep learning. That’s no longer true: you can take an existing model (eg ResNet for image classification) and train on top of it. ResNet can classify images as 1,000 classes (house, cat, etc)—training an extra few hundred images of e.g. Bobcats vs Cougars only takes a couple of minutes on a GPU and can give you crazily accurate results. It works because the pre-trained model can already pick up really subtle details—fur patterns, ear shapes etc—so you only need to train a few more layers on it for it to be able to classify against the patterns in your new set of training images. And this doesnt just work for image classification! Natural language processing benefits from transfer learning too: take an existing model trained on the entire corpus of Wikipedia (so it knows patterns from sentence structures) and you can build IMDB sentiment analysis on top. That’s in lesson 4. # 26th January 2019, 12:32 am