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Perceptrons: An Introduction to Computational Geometry, Expanded Edition

Author: Marvin Minsky, Seymour A. Papert

Three-Sentence Summary

Perceptrons, written by Marvin Minsky and Seymour A. Papert, delves into the concept of artificial neural networks and their limitations. The book explores the capabilities of perceptrons, single-layer neural networks, and discusses the challenges they face in solving complex problems. With a new foreword by Léon Bottou in this reissue of the 1988 Expanded Edition, readers can gain insights into the foundational principles of machine learning.

Extended Summary

Perceptrons is a seminal work that examines the fundamental concepts of artificial neural networks, specifically focusing on perceptrons - single-layer neural networks. Marvin Minsky and Seymour A. Papert delve into the capabilities and limitations of these simple models, discussing their inability to solve non-linearly separable problems. The book provides a critical analysis of early attempts at using perceptrons for pattern recognition tasks, shedding light on the challenges faced in training these models effectively.

In this reissue of the 1988 Expanded Edition with a new foreword by Léon Bottou, readers are introduced to the historical context in which perceptrons were developed and the impact they had on shaping subsequent developments in machine learning. The book not only serves as a historical artifact in the field but also offers valuable insights into understanding the foundations of neural network theory.

Through detailed explanations and examples, Minsky and Papert highlight key concepts such as linear separability, convergence properties of learning algorithms, and the limitations posed by single-layer architectures. The authors also discuss how multi-layer networks can overcome these limitations, laying the groundwork for more sophisticated deep learning models that are prevalent today.

Overall, Perceptrons is a thought-provoking read for anyone interested in delving into the roots of artificial intelligence and gaining a deeper understanding of how neural networks have evolved over time.

Key Points

  1. Perceptrons are single-layer neural network models that have limitations in solving complex problems.
  2. The book critiques early attempts at using perceptrons for pattern recognition tasks.
  3. Multi-layer networks emerged as solutions to overcome the limitations posed by single-layer architectures.

Who Should Read

Ideal for students and professionals in artificial intelligence, machine learning, or computer science fields who want to explore the foundational principles of neural networks. Readers interested in understanding how early developments in perceptron theory influenced modern deep learning approaches will find this book insightful.

About the Authors

Marvin Minsky was an American cognitive scientist who co-founded MIT's AI laboratory and made significant contributions to artificial intelligence research. Seymour A. Papert was a mathematician and computer scientist known for his work on educational technology and pioneering research in artificial intelligence.

Further Reading