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Artificial Neural Network - Building Intelligent Systems for Robotic Autonomy and Adaptation - cover

Artificial Neural Network - Building Intelligent Systems for Robotic Autonomy and Adaptation

Fouad Sabry

Publisher: One Billion Knowledgeable

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Summary

1: Artificial neural network: Explore the basics and broad significance of neural networks.
 
2: Perceptron: Understand the building blocks of singlelayer learning models.
 
3: Jürgen Schmidhuber: Discover the pioneering research behind modern networks.
 
4: Neuroevolution: Examine genetic approaches to optimizing neural architectures.
 
5: Recurrent neural network: Investigate networks with memory for sequential data.
 
6: Feedforward neural network: Analyze networks where data moves in a single direction.
 
7: Multilayer perceptron: Learn about layered structures enhancing network depth.
 
8: Quantum neural network: Uncover the potential of quantumassisted learning models.
 
9: ADALINE: Study adaptive linear neurons for pattern recognition.
 
10: Echo state network: Explore dynamic reservoir models for temporal data.
 
11: Spiking neural network: Understand biologically inspired neural systems.
 
12: Reservoir computing: Dive into specialized networks for timeseries analysis.
 
13: Long shortterm memory: Master architectures designed to retain information.
 
14: Types of artificial neural networks: Differentiate between various network models.
 
15: Deep learning: Grasp the depth and scope of multilayered networks.
 
16: Learning rule: Explore methods guiding neural model training.
 
17: Convolutional neural network: Analyze networks tailored for image data.
 
18: Vanishing gradient problem: Address challenges in network training.
 
19: Bidirectional recurrent neural networks: Discover models that process data in both directions.
 
20: Residual neural network: Learn advanced techniques to optimize learning.
 
21: History of artificial neural networks: Trace the evolution of this transformative field.
Available since: 12/17/2024.
Print length: 263 pages.

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