Artificial Neural Network - Building Intelligent Systems for Robotic Autonomy and Adaptation
Fouad Sabry
Editora: One Billion Knowledgeable
Sinopse
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.
Disponível desde: 17/12/2024.
Comprimento de impressão: 263 páginas.
