Summary of DeepFM | Lecture 82 (Part 2) | Applied Deep Learning (Supplementary)

00:00:00 - 00:05:00

This lecture by Dr. Yoshua Bengio explains how deep learning can be used to predict how much money a company will make from online advertisements. He discusses how to perform feature engineering in order to reduce the number of high-dimensional features in the data, and demonstrates how to use a deep factorization machine to embed features into a data set.

• 00:00:00 In this lecture, Dr. Yoshua Bengio explains how deep learning can be used to predict how much money a company will make from online advertisements. He also discusses how to perform feature engineering in order to reduce the number of high-dimensional features in the data. Finally, he demonstrates how to use a deep factorization machine to embed features into a data set.
• 00:05:00 This lecture discusses how deep learning works, including an embedded layer that allows for efficient feature engineering. The lecture also compares deep learning to other machine learning methods, and explains how deep learning can outperform neural factorization machines in terms of performance.