'Weight space' diaporamas de présentation

Smart Home Technologies

938 views • 74 slides

From Biological to Artificial Neural Networks

1.17k views • 58 slides

Multi layer feed-forward NN FFNN

Multi layer feed-forward NN FFNN. We consider a more general network architecture: between the input and output layers there are hidden layers, as illustrated below. Hidden nodes do not directly send outputs to the external environment.

2.7k views • 44 slides

Concept Learning Algorithms

Concept Learning Algorithms. Come from many different theoretical backgrounds and motivations Behaviors related to human learning Some biologically inspired, others not. Neural Networks. Nearest Neighbor. Tree Learners. Utilitarian (just get good result). Biologically- Inspired.

553 views • 39 slides

Concept Learning Algorithms

Concept Learning Algorithms. Come from many different theoretical backgrounds and motivations Behaviors related to human learning Some biologically inspired, others not. Neural Networks. Nearest Neighbor. Tree Learners. Utilitarian (just get good result). Biologically- Inspired.

392 views • 39 slides

Glasgow 02/02/04

481 views • 36 slides

Biological and Artificial Neuron

Neural Networks. Learning Processes. Biological and Artificial Neuron. Weights, need to be determined. Biological neuron. Bias, need to be determined. Artificial neuron. Neural Networks. Learning Processes. Application of Neural Networks. Function approximation and prediction

357 views • 25 slides

Biological and Artificial Neuron

Neural Networks. Learning Processes. Biological and Artificial Neuron. Weights, need to be determined. Biological neuron. Bias, need to be determined. Artificial neuron. Neural Networks. Learning Processes. Application of Neural Networks. Function approximation and prediction

408 views • 25 slides

Interstellar Travel

426 views • 24 slides

Biological and Artificial Neuron

Neural Networks. Learning Processes. Biological and Artificial Neuron. Weights, need to be determined. Biological neuron. Bias, need to be determined. Artificial neuron. Neural Networks. Learning Processes. Application of Neural Networks. Function approximation and prediction

355 views • 24 slides

On the capacity of unsupervised recursive neural networks for symbol processing

On the capacity of unsupervised recursive neural networks for symbol processing . Prof. Dr. Barbara Hammer Computational Intelligence Group Institute of Computer Science Clausthal University of Technology Nicolas Neubauer, M.Sc. Neural Information Processing Group

352 views • 23 slides