Artificial Neural Networks
May 1, 2024
Updated May 11, 2025
25 minute read
Artificial Neural Networks (ANNs) are computational systems inspired by the intricate network of neurons in the biological brain. These networks are at the core of many advancements in artificial intelligence, designed to learn from data, identify patterns, and make decisions or predictions. ANNs consist of interconnected processing units, often called artificial neurons or nodes, organized in layers. This structure allows them to process complex information and solve problems that might be challenging for traditional programming approaches.
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Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Artificial Neural Networks.
A comprehensive introduction to the field of artificial neural networks, covering topics such as supervised and unsupervised learning, network architectures, and applications in a variety of domains.
A detailed treatment of artificial neural networks for pattern recognition, including topics such as feature selection, network design, and training algorithms.
A practical guide to deep learning with Python, including a chapter on artificial neural networks.
An overview of artificial neural networks in German, covering topics such as network architectures, learning algorithms, and applications.
A detailed treatment of variational autoencoders, a type of artificial neural network that can be used to generate new data and learn the underlying structure of data.
A detailed treatment of autoencoders, a type of artificial neural network that can be used to learn efficient representations of data.
A detailed treatment of Bayesian neural networks, a type of artificial neural network that can be used to learn the uncertainty in predictions.
A practical guide to machine learning with Python, including a chapter on artificial neural networks.
A practical guide to machine learning, including a chapter on artificial neural networks.
A comprehensive introduction to reinforcement learning, a type of machine learning that is well-suited for tasks that require an agent to learn from its interactions with an environment.
A detailed treatment of neuro-symbolic artificial intelligence, a type of artificial intelligence that combines artificial neural networks with symbolic reasoning.
A comprehensive textbook on artificial intelligence, including a chapter on artificial neural networks.
For more information about how these books relate to this course, visit:
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