Machine Learning Curricula
Bringing the people back in: Contesting benchmark machine learning datasets was a paper from July, 2020, by Emily Denton and team. It is an interogation of how datasets in machine learning are made and how they influence the field. The work motivates the need for genealogical methods for datasets so that we can trace their history and ensure that users are sufficiently aware of what biases they…
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves was a paper from September, 2020, by Luke Metz and co. It is another step towards replacing hand-designed features with learned functions, this time the optimizer. This has been a three year journey for Luke; listen to him describe what he’s learned along the way and where…
Characterising Bias in Compressed Models by Sara Hooker et al highlighted where the lunch was getting paid when it came to modern deep learning compression techniques. All of these models we use on a pervasive basis, in her phones, on social feeds, etc, they all use compression. Are we compromising what we want when we apply this everywhere? More particularly, are we affecting some groups more…
T5 by Colin Raffel et al is an important work in the NLP literature. The idea behind it was to perform a gigantic study on a wide array of methods and scientifically assess what worked. They then combined those working methods into a single model called … T5. It performs well in every common NLP task, from summarization to translation to question answering.
[Editor’s Note: This class was a part of the 2019 DFL Jane Street Fellowship.]
[Editor’s Note: This class was a part of the 2019 DFL Jane Street Fellowship.] This guide would not have been possible without the help and feedback from many people.
[Editor’s Note: This class was a part of the 2019 DFL Jane Street Fellowship.]
This guide would not have been possible without the help and feedback from many people.
[Editor’s Note: We are especially proud of this one. James and his group went above and beyond the call of duty and made a guide from their class that we feel is especially superb for understanding their target paper. Moving forward, he has forced us to up our game because it will be hard to release a curriculum that is not as strong as this one. We highly recommend earnestly studying with this at…
After we launched Depth First Learning last year, we wanted to keep the momentum and continue outputting high-quality study guides for machine learning. Subsequently, we launched the Depth First Learning Fellowship with funding provided by Jane Street.
