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Neuro-Symbolic AI

Neuro-Symbolic AI

By : Alexiei Dingli, David Farrugia
3.7 (6)
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Neuro-Symbolic AI

Neuro-Symbolic AI

3.7 (6)
By: Alexiei Dingli, David Farrugia

Overview of this book

Neuro-symbolic AI offers the potential to create intelligent systems that possess both the reasoning capabilities of symbolic AI along with the learning capabilities of neural networks. This book provides an overview of AI and its inner mechanics, covering both symbolic and neural network approaches. You’ll begin by exploring the decline of symbolic AI and the recent neural network revolution, as well as their limitations. The book then delves into the importance of building trustworthy and transparent AI solutions using explainable AI techniques. As you advance, you’ll explore the emerging field of neuro-symbolic AI, which combines symbolic AI and modern neural networks to improve performance and transparency. You’ll also learn how to get started with neuro-symbolic AI using Python with the help of practical examples. In addition, the book covers the most promising technologies in the field, providing insights into the future of AI. Upon completing this book, you will acquire a profound comprehension of neuro-symbolic AI and its practical implications. Additionally, you will cultivate the essential abilities to conceptualize, design, and execute neuro-symbolic AI solutions.
Table of Contents (12 chapters)
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Introducing popular neural network architectures

In this chapter, we explore some of the most popular ANN architectures beyond the basic single-layer and multilayer perceptrons. The recurrent neural network (RNN) is a feed-forward network that incorporates temporal relationships and is widely used in applications ranging from sentence autocompletion to stock market predictions. However, RNNs suffer from the vanishing gradient problem, which hinders their learning ability. Competitive networks, such as Kohonen networks and self-organizing maps, classify inputs without supervision, while Hopfield networks, a special ANN with every node connected to every other node, act as associative memory and tend to converge based on similarities. Boltzmann machines (BMs) and restricted Boltzmann machines (RBMs) are variants of the Hopfield network that have additional restrictions and are trained using unsupervised learning approaches, making them great at extracting discriminative features from...

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