AlphaGo introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play.

Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. AlphaGo also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks.

Read more:

https://googleblog.blogspot.com.au/2016/01/alphago-machine-learning-game-go.html
http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html

AlphaGo
8 new photos added to shared album

After LAMP (Linux, Apache, MySQL, PHP) stack die in slow death, and over-hyped MEAN stack (MongoDB, ExpressJS, AngularJS, NodeJS) has became so yesterday, now new kid in the block is SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka).

Data processing platforms architectures with SMACK: Spark, Mesos, Akka, Cassandra and Kafka
This post is a follow-up of the talk given at Big Data AW meetup in Stockholm and focused on different use cases and design approaches for building scalable data processing platforms with SMACK(Spark, Mesos, Akka, Cassandra, Kafka) stack. While…