{"id":19896,"date":"2016-05-16T19:51:42","date_gmt":"2016-05-17T03:51:42","guid":{"rendered":"https:\/\/www.altoros.com\/blog\/?p=19896"},"modified":"2021-02-15T16:31:05","modified_gmt":"2021-02-15T13:31:05","slug":"tensorflow-in-action-tensorboard-training-a-model-and-deep-q-learning","status":"publish","type":"post","link":"https:\/\/www.altoros.com\/blog\/tensorflow-in-action-tensorboard-training-a-model-and-deep-q-learning\/","title":{"rendered":"TensorFlow in Action: TensorBoard, Training a Model, and Deep Q-learning"},"content":{"rendered":"

\"videos-and-slides-from-tensorflow-meetup-in-london-march-2016\"<\/p>\n

In data science, Q-learning<\/a> represents an approach to learning about a state space and following the optimal policy thereafter. A classic example is a robot navigating a maze. Below are the videos from the TensorFlow London meetup<\/a> featuring deep dive into TensorFlow’s architecture and a Q-learning example.<\/p>\n

 <\/p>\n

\n
\n

Table of Contents<\/p>\nToggle<\/span><\/path><\/svg><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n