Artificial Neural Networks - Introduction -
... Learning = learning by adaptation For example: Animals learn that the green fruits are sour and the yellowish/reddish ones are sweet. The learning happens by adapting the fruit picking behavior. Learning can be perceived as an optimisation process. When an ANN is in its SUPERVISED training or lear ...
... Learning = learning by adaptation For example: Animals learn that the green fruits are sour and the yellowish/reddish ones are sweet. The learning happens by adapting the fruit picking behavior. Learning can be perceived as an optimisation process. When an ANN is in its SUPERVISED training or lear ...
Pattern Recognition and Feed-forward Networks
... The goal in pattern recognition is to use a set of example solutions to some problem to infer an underlying regularity which can subsequently be used to solve new instances of the problem. Examples include hand-written digit recognition, medical image screening and fingerprint identification. In the ...
... The goal in pattern recognition is to use a set of example solutions to some problem to infer an underlying regularity which can subsequently be used to solve new instances of the problem. Examples include hand-written digit recognition, medical image screening and fingerprint identification. In the ...
Self Organized Maps (SOM)
... adjusted to make them more like the input vector. The closer a node is to the BMU, the more its weights get altered. Repeat step 2 for N iterations. http://www.ai-junkie.com/ann/som/som2.html ...
... adjusted to make them more like the input vector. The closer a node is to the BMU, the more its weights get altered. Repeat step 2 for N iterations. http://www.ai-junkie.com/ann/som/som2.html ...
Neural Networks for Data Mining
... developed all sorts of schemata to decrease network complexity. This results in more complex learning rules, that for instance cause weights to be zero (corresponding to the elimination of weights). – It might be useful to train several networks at the same time, giving an ensemble of networks. Thei ...
... developed all sorts of schemata to decrease network complexity. This results in more complex learning rules, that for instance cause weights to be zero (corresponding to the elimination of weights). – It might be useful to train several networks at the same time, giving an ensemble of networks. Thei ...
Full project report
... An ANN without hidden layers is only able to learn to identify linearly separable problems (problems where the results can be separated as being classified to a single class using a linear function). Since our problem is more complex, we needed to add hidden layers between the input and output layer ...
... An ANN without hidden layers is only able to learn to identify linearly separable problems (problems where the results can be separated as being classified to a single class using a linear function). Since our problem is more complex, we needed to add hidden layers between the input and output layer ...
Presentation
... Find the result based on current weights Subtract result from desired result = error term Look at each initial node individually ...
... Find the result based on current weights Subtract result from desired result = error term Look at each initial node individually ...
Neural Networks A Statistical View
... OLS with 3 independent and 1 dependent variables would have a maximum of 3 coefficients and 1 intercept With 2 dependent variables, it would require Canonical Correlation (general linear model) and the same number of coefficients ANN (with one hidden layer) has 15 coefficients (weights) and activati ...
... OLS with 3 independent and 1 dependent variables would have a maximum of 3 coefficients and 1 intercept With 2 dependent variables, it would require Canonical Correlation (general linear model) and the same number of coefficients ANN (with one hidden layer) has 15 coefficients (weights) and activati ...
ANN
... • Difference between the generated value and the desired value is the error • The overall error is expressed as the root mean squares (RMS) of the errors (both –ve and +ve) • Training minimized RMS by altering the weights and bias, through many passes of the training data. • This search for weights ...
... • Difference between the generated value and the desired value is the error • The overall error is expressed as the root mean squares (RMS) of the errors (both –ve and +ve) • Training minimized RMS by altering the weights and bias, through many passes of the training data. • This search for weights ...
PowerPoint
... • In Hebbian networks, all neurons can fire at the same time • Competitive learning means that only a single neuron from each group fires at each time step • Output units compete with one another. • These are winner takes all units (grandmother cells) ...
... • In Hebbian networks, all neurons can fire at the same time • Competitive learning means that only a single neuron from each group fires at each time step • Output units compete with one another. • These are winner takes all units (grandmother cells) ...
Artificial intelligence: Neural networks
... A neural network is a simula on of the algorithm, that the brain uses to process any kind of data. It has an input layer, one or more hidden layers and an output layer. In machine learning and deep learning problems, a neural network is one of the most widely used algorithms which is used to process ...
... A neural network is a simula on of the algorithm, that the brain uses to process any kind of data. It has an input layer, one or more hidden layers and an output layer. In machine learning and deep learning problems, a neural network is one of the most widely used algorithms which is used to process ...
Modern Artificial Intelligence
... Deep learning has made it possible to learn end-to-end without pre-programming. Artificial General Intelligence is looking for agents that successfully operate across a wide range of tasks. ...
... Deep learning has made it possible to learn end-to-end without pre-programming. Artificial General Intelligence is looking for agents that successfully operate across a wide range of tasks. ...
شبکه های عصبی
... معایب و مزایا استفاده از شبکه ها Neural Networks can be extremely complex and hard to use The programs are filled with settings you must input and a small amount of data will cause your predictions to have error The results can be very hard to interpret as well Dead-end situations are hard to avo ...
... معایب و مزایا استفاده از شبکه ها Neural Networks can be extremely complex and hard to use The programs are filled with settings you must input and a small amount of data will cause your predictions to have error The results can be very hard to interpret as well Dead-end situations are hard to avo ...
Bump attractors and the homogeneity assumption
... Solutions • Fine tuning properties of each neuron. • Network learns to tune itself through an activity-dependent mechanism. – “Activity-dependent scaling of synaptic weights, which up- or downregulates excitatory inputs so that the long term average firing rate is similar for each neuron” (Renart, ...
... Solutions • Fine tuning properties of each neuron. • Network learns to tune itself through an activity-dependent mechanism. – “Activity-dependent scaling of synaptic weights, which up- or downregulates excitatory inputs so that the long term average firing rate is similar for each neuron” (Renart, ...
Artificial Neural Network Quiz
... 3. Which of the following is true? Single layer associative neural networks do not have the ability to: (i) perform pattern recognition (ii) find the parity of a picture (iii)determine whether two or more shapes in a picture are connected or not a) (ii) and (iii) are true b) (ii) is true c) All of t ...
... 3. Which of the following is true? Single layer associative neural networks do not have the ability to: (i) perform pattern recognition (ii) find the parity of a picture (iii)determine whether two or more shapes in a picture are connected or not a) (ii) and (iii) are true b) (ii) is true c) All of t ...
Genetic Algorithms for Optimization
... Hh: the output of h-th neuron in hidden layer Ii: the value of i-th input wih: the weight of the connection from i-th input to h-th neuron in hidden layer ...
... Hh: the output of h-th neuron in hidden layer Ii: the value of i-th input wih: the weight of the connection from i-th input to h-th neuron in hidden layer ...
Programming task 5
... • Choose the weights for the Kohonen network uniformly distributed inside the input space domain. • In this task we will keep the weights in the first layer the same all the time, i.e. no training of the Kohonen network is needed. OBS: If the input vectors are non-uniformly distributed we need to tr ...
... • Choose the weights for the Kohonen network uniformly distributed inside the input space domain. • In this task we will keep the weights in the first layer the same all the time, i.e. no training of the Kohonen network is needed. OBS: If the input vectors are non-uniformly distributed we need to tr ...
Catastrophic interference
Catastrophic Interference, also known as catastrophic forgetting, is the tendency of a artificial neural network to completely and abruptly forget previously learned information upon learning new information. Neural networks are an important part of the network approach and connectionist approach to cognitive science. These networks use computer simulations to try and model human behaviours, such as memory and learning. Catastrophic interference is an important issue to consider when creating connectionist models of memory. It was originally brought to the attention of the scientific community by research from McCloskey and Cohen (1989), and Ractcliff (1990). It is a radical manifestation of the ‘sensitivity-stability’ dilemma or the ‘stability-plasticity’ dilemma. Specifically, these problems refer to the issue of being able to make an artificial neural network that is sensitive to, but not disrupted by, new information. Lookup tables and connectionist networks lie on the opposite sides of the stability plasticity spectrum. The former remains completely stable in the presence of new information but lacks the ability to generalize, i.e. infer general principles, from new inputs. On the other hand, connectionst networks like the standard backpropagation network are very sensitive to new information and can generalize on new inputs. Backpropagation models can be considered good models of human memory insofar as they mirror the human ability to generalize but these networks often exhibit less stability than human memory. Notably, these backpropagation networks are susceptible to catastrophic interference. This is considered an issue when attempting to model human memory because, unlike these networks, humans typically do not show catastrophic forgetting. Thus, the issue of catastrophic interference must be eradicated from these backpropagation models in order to enhance the plausibility as models of human memory.