
LM101-036: How to Predict the Future from the Distant Past using Recurrent Neural Networks
About this episode
In this episode, we discuss the problem of predicting the future from not only recent events but also from the distant past using Recurrent Neural Networks (RNNs). A example RNN is described which learns to label images with simple sentences. A learning machine capable of generating even simple descriptions of images such as these could be used to help the blind interpret images, provide assistance to children and adults in language acquisition, support internet search of content in images, and enhance search engine optimization websites containing unlabeled images. Both tutorial notes and advanced implementational notes for RNNs can be found in the show notes at: www.learningmachines101.com .
Get every episode summarized
Each time Learning Machines 101 publishes, we email you a written briefing from the transcript — the topics, who appeared, and any specific claims, with the ad reads skipped.
Email me new episodesFree for 3 shows. No card needed.
Hosts & guests
No transcript yet
This episode has not been transcribed. Request it and it moves to the front of the queue.
More episodes
More from Learning Machines 101

LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes
Learning Machines 101

LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges
Learning Machines 101

LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems
Learning Machines 101

LM101-083: Ch5: How to Use Calculus to Design Learning Machines
Learning Machines 101