In supervised learning it is often all about labels. Do we have enough labeled data? If not,MEHR ERFAHREN
In 2010 ImageNet finally ended the AI winter and gave machines the sense of sight. Within the following years dramatic improvements in tasks such as image classification and object detection lead to innovations like face ID and autonomous driving. Recently, similar developments happened in the field of natural language. Using Attention mechanism and transformers tasks such as question answering and text summarization reached new benchmarks.
This talk will not only explain those, but point out how Transfer Learning and open source models such as Google Bert will open the field to new innovations in AI.
According to the Data Science Survey conducted by JetBrains in 2018, Jupyter/IPython notebooks are the most popularMEHR ERFAHREN