Berkeley Deep #Reinforcement #Learning(RL) Bootcamp - lecture videos and slides http://goo.gl/viywiV

"The Bitter Lesson"
The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin.
In computer chess, the methods that defeated the world champion, Kasparov, in 1997, were based on massive, deep search (…) A similar pattern of research progress was seen in computer Go, only delayed by a further 20 years.
One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation, even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning.
Rich Sutton, March 13, 2019: http://www.incompleteideas.net/IncIdeas/BitterLesson.html
#Learning #ReinforcementLearning #Search

What’s New in Deep Learning Research: Learning and Teaching the West World Way
#towardsdatascience #datascience #news #learning
https://towardsdatascience.com/whats-new-in-deep-learning-research-learning-and-teaching-the-west-world-way-659892b2e452

How Learning Can Guide Evolution
Geoffrey E. Hinton and Steven J. Nowlan : http://www.cs.toronto.edu/~hinton/absps/evolution.htm
#Learning #Evolution

​​CREME – python library for online ML

All the tools in the library can be updated with a single observation at a time, and can therefore be used to learn from streaming data.

The model learns from one observation at a time, and can therefore be updated on the fly. This allows to learn from massive datasets that don't fit in main memory. Online machine learning also integrates nicely in cases where new data is constantly arriving. It shines in many use cases, such as time series forecasting, spam filtering, recommender systems, CTR prediction, and IoT applications. If you're bored with retraining models and want to instead build dynamic models, then online machine learning might be what you're looking for.

Here are some benefits of using creme (and online machine learning in general):
• incremental – models can update themselves in real-time
• adaptive – models can adapt to concept drift
• production-ready – working with data streams makes it simple to replicate production scenarios during model development
• efficient – models don't have to be retrained and require little compute power, which lowers their carbon footprint


api reference: https://creme-ml.github.io/content/api.html
github: https://github.com/creme-ml/creme

#ml #online #learning