'Hidden layer' presentation slideshows

Secrets of Neural Network Models

2.13k views • 174 slides

Ch. 11 :Introduction to RNN, LSTM

Ch. 11 :Introduction to RNN, LSTM. RNN ( Recurrent neural network ) LSTM ( Long short-term memory ) KH Wong. Overview. Introduction Concept of RNN ( Recurrent neural network ) ? The Gradient vanishing problem LSTM theory and concept LSTM Numerical example. Introduction.

4.64k views • 103 slides

Objectives of Presentation

Data Mining: Neural Network Applications by Louise Francis CAS Annual Meeting, Nov 11, 2002 Francis Analytics and Actuarial Data Mining, Inc. louise_francis@msn.com. Objectives of Presentation. Introduce insurance professionals to neural networks

658 views • 48 slides

Genetic algorithms and hybrid systems Will genetic algorithms work for my problem? The travelling salesman problem Wil

Lecture 15. Knowledge engineering:. Genetic algorithms and hybrid systems Will genetic algorithms work for my problem? The travelling salesman problem Will a hybrid system work for my problem? Neuro -fuzzy decision-support systems Time-series prediction Summary.

613 views • 39 slides

2806 Neural Computation Radial Basis Function Networks Lecture 5

2806 Neural Computation Radial Basis Function Networks Lecture 5. 2005 Ari Visa. Agenda. Some historical notes Radial Basis Function Networks Some theory Regularization Networks Generalized Radial-Basis Function Networks Approximating properties of RBF Networks Learning Strategies

830 views • 36 slides

Artificial Neural Networks

750 views • 33 slides

CS 343: Artificial Intelligence Neural Networks

CS 343: Artificial Intelligence Neural Networks. Raymond J. Mooney University of Texas at Austin. Neural Networks. Analogy to biological neural systems, the most robust learning systems we know. Attempt to understand natural biological systems through computational modeling.

610 views • 32 slides

Artificial Neural Networks

Artificial Neural Networks. Overview. Computational units and architectures Learning in perceptrons Learning in Multilayer feed-forward nets. Neural Nets. Composed of basic units and weighted links between them The basic units (or nodes) are an idealization of neurons

523 views • 32 slides

Chapter 4 Supervised learning: Multilayer Networks II

Chapter 4 Supervised learning: Multilayer Networks II. Other Feedforward Networks. Madaline Multiple adalines (of a sort) as hidden nodes Weight change follows minimum disturbance principle Adaptive multi-layer networks Dynamically change the network size (# of hidden nodes)

476 views • 31 slides

The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects

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Learning in Neural and Belief Networks

Learning in Neural and Belief Networks. Feed Forward Neural Network. 2001? 3? 28? 20013329 ???. Contents. How the Brain works Neural Networks Perceptrons. Introduction. Two view points in this chapter Computational view points : representing function using network

666 views • 29 slides

Neural Networks

Neural Networks. Tuomas Sandholm Carnegie Mellon University Computer Science Department. How the brain works. Synaptic connections exhibit long-term changes in the connection strengths based on patterns seen. Comparing brains with digital computers. Parallelism Graceful degradation

453 views • 28 slides

Introduction to Neural Networks

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2806 Neural Computation Temporal Processing Lecture 11

2806 Neural Computation Temporal Processing Lecture 11. 2005 Ari Visa. Agenda. Some historical notes Some theory Some network architecture C onclusions . Some Historical Notes . z-transform A single neuron -> an adaptive filter

407 views • 25 slides

Linear Separability

3. 3. 3. x 2. x 2. x 2. 2. 2. 2. 1. 1. 1. -3. -3. -3. -2. -2. -2. -1. -1. -1. 1. 1. 1. 2. 2. 2. 3. 3. 3. x 1. x 1. x 1. -1. -1. -1. -2. -2. -2. -3. -3. -3. Linear Separability. Input space in the two-dimensional case (n = 2):. 1. 1. 1. 0. 0. 0.

408 views • 25 slides

Web Servers for Predicting Protein Secondary Structure (Regular and Irregular)

Web Servers for Predicting Protein Secondary Structure (Regular and Irregular). Protein Sequence +. Dr. G.P.S. Raghava, F.N.A. Sc. Bioinformatics Centre Institute of Microbial Technology Chandigarh, INDIA E-mail: raghava@imtech.res.in Web: www.imtech.res.in/raghava/

256 views • 24 slides

Application of Back-Propagation neural network in data forecasting

1.13k views • 23 slides

Myopic and non-myopic agent optimization in game theory, economics, biology and artificial intelligence

Myopic and non-myopic agent optimization in game theory, economics, biology and artificial intelligence. Kae Nemoto Quantum Information Science National Institute of Informatics, Japan Email: nemoto@nii.ac.jp. Michael J Gagen Institute of Molecular Bioscience University of Queensland

455 views • 23 slides

Backpropagation learning

Backpropagation learning. Simple vs. multilayer perceptron. Hidden layer problem. Radical change for the supervised learning problem. No desired values for the hidden layer. The network must find its own hidden layer activations. Generalized delta rule.

402 views • 22 slides

AI in games

AI in games. Roger Crawfis/Eric Fosler-Lussier CSE 786 Game Design. AI vs. AI for games. AI for games poses a number of unique design challenges Traditional AI: Made to handle unseen inputs, large state space Too many options possible to compute an exact optimal solution

317 views • 14 slides