Lifelong Machine Learning (Zhiyuan Chen, et al)

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Lifelong Machine Learning (Zhiyuan Chen, et al)

This book serves as an introduction to a cutting-edge machine learning paradigm that continuously picks up new information by collecting prior knowledge, which it then applies to learning and problem-solving in the future.

In contrast, the prevalent machine learning paradigm of today learns in isolation. Given a training dataset, it applies a machine learning algorithm to the dataset to create a model, which is subsequently applied in the application for which it was designed.

It makes no effort to retain what is learned and apply it to new learning. Humans, in contrast to this isolated system, can learn from a small number of instances efficiently since our learning is heavily knowledge-driven, allowing us to pick up new information quickly and with minimal effort. Since without it, an AI system cannot be said to be genuinely intelligent, lifelong learning seeks to replicate this ability.

Ebook Details

About the Authors
  • At Google, Zhiyuan Chen works as a staff software engineer and tech lead manager. At the University of Illinois at Chicago, he earned his Ph.D. in Lifelong Machine Learning for Topic Modeling and Classification under the guidance of Professor Bing Liu.
  • The University of Illinois at Chicago's Bing Liu is a distinguished professor of computer science there.
Published Date / Year
1st Ed (2016); 2nd Ed (2018); eBook (Final Draft)
146 pages
eBook Format
PDF (145 pages)

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