Network structure inspired by simplified models of biological neurons (brain cells). Neural networks are trained to "learn" by supervised and unsupervised techniques, and can be used to solve optimization problems, approximation problems, classify patterns, and combinations thereof.

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3
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33 views

Genetic Algorithm, Neural Network, Deep Learning, Machine Learning Similarities and Applications? [on hold]

I am a computer engineering student and trying to get the idea behind all these Artificial Intelligence Concepts and applications. I know little theoretically about machine learning and some high ...
2
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0answers
18 views

ANN - Backpropagation with multiple output neurons

Can I utilize the backpropagation algorithm in a layered, feed-forward ANN in instances where there are multiple output neurons? If so, how? Links to (somewhat) comprehensible resources would be ...
1
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0answers
6 views

How do NEF networks differ from ANNs in applications?

As I've described here, the Neural Engineering Framework (NEF) has some functional similarities to Artificial Neural Networks (ANNs). Naturally, there is also an overlap in applications between ...
0
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0answers
14 views

Is current hardware adequate for neural networks ? Are there more adequate hardware?

If you have a large neural network and you use more than 10 cores, it will be limited by the fact each core will need to read/write data that it can't access fast enough. I've read about some samsung ...
1
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0answers
20 views

backpropagation algorithm seems to be forcing output values to middle than extremes

I have been playing around with artificial neural networks lately, specifically with the prospect of trying to replace the contrastive divergence algorithm with some type of evolutionary metaheuristic ...
3
votes
1answer
80 views

How does the momentum term for backpropagation algorithm work?

When updating the weights of a neural network using the backpropagation algorithm with a momentum term, should the learning rate be applied to the momentum term as well? Most of the information I ...
0
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1answer
19 views

ANN architectures that perform faster/efficient prediction over time?

ANN when compared to biological neural systems have this common concept of reducing error in prediction over time (training) and becoming more good at predicting correctly. But there is one behavior ...
5
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1answer
61 views

How exactly do you calculate the hidden layer gradients in the backpropagation algorithm?

I have been going through the description of the backpropagation algorithm found here. and I am having a bit of trouble getting my head around some of the linear algebra. Say I have a final output ...
0
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0answers
6 views

Can HTM CLA be applied to the problem of autonomous navigation of bots or drones?

See: http://numenta.org/resources/HTM_CorticalLearningAlgorithms.pdf Hierarchical Temporal Memory (HTM) is a detailed computational theory of the neocortex. At the core of HTM are time-based learning ...
1
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1answer
27 views

are the activations of hidden nodes in an ANN binary or real valued?

this may seem to be a pretty basic question, but it is something i have been puzzling over for some time. when calculating the activations of nodes in a hidden layer in an ANN using sigmoid neurons ...
0
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0answers
19 views

What math is required for machine learning, neural networks etc.? [duplicate]

I'm planning to start learning more in depth about neural networks and machine learning. Can someone tell me which math will I need the most, and also recommend me good books or internet lessons in ...
1
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1answer
29 views

Neural Network Weights per input nodes

Sorry, I'm really new to neural networks and this question is probably pretty obvious. If you have any resource that can help clarify these concepts to me it would be much appreciated. The way I ...
1
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0answers
64 views

Improving MSE as fitness function for a genetic algorithm

I am implementing an autoencoder neural network in matlab, the weights of which are being optimised by a genetic algorithm. At the moment I am working on the first layer, trying to get an improved ...
1
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0answers
44 views

Disadvantages to using simple step functions for activation in neural networks?

From what I have read, the main advantage to using tanh(x) or sigmoid(x) as an activation function for neural networks is that it is very easily differentiable. I am trying to implement a neural ...
0
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1answer
68 views

How to reconstruct the image from a neural network output?

I am trying to use the genetic algorithm to optimise a multi-layered neural network for image classification (i am using a subset of the MNIST handwritten digit data set as my initial dataset, but ...
2
votes
2answers
57 views

Is it possible to implement a Neural Network using a graph data structure?

I'm trying to implement a feedforward neural network using a graph. The thing is: I haven't found any example in which is used a graph data structure. So far the examples I've found used arrays. Can ...
2
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5answers
203 views

Machine Learning and Neural Networks for High School Students

I hope this question is appropriate for this forum. In this summer I am giving a 3-day workshop on machine learning and neural networks for advanced and very enthusiastic high school students which ...
1
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2answers
59 views

Are neural networks dynamical systems?

Dynamical systems are those whose evolution can be described by a rule, evolves with time and is deterministic. In this context can I say that Neural networks have a rule of evolution which is the ...
2
votes
1answer
49 views

Progressive discrete multifunction optimization

I have a set of functions $F$. The functions effectively take a set $S$ that is always a subset of a global set of all possible values $G$, where $|G|>1000$. (alternatively, they could take a ...
2
votes
1answer
45 views

Closed form solution for a single layer linear perceptron

Let f be a one-layer neural network which is linear (ie. no activation function). Let it have $p$ inputs and $q$ outputs. These are fully connected by weights $W$. We have $n$ inputs $x \in ...
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0answers
49 views

Where to start studying about HTM?

I am looking for references (pedagogic and beginner friendly!) to these two topics, hierarchical temporal memory algorithms applied to deep planning problems (multi-layer) neural networks trained ...
3
votes
1answer
33 views

Training given pairs of similar values, not labels

I have pairs of "similar" values $(x_i, y_i)$ drawn from a space $x_i, y_i \in S$, and want to train a neural network $N$ such that $N(x_i)$ would be "close" to $N(y_i)$ for all $i$, yet, to make it ...
1
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1answer
25 views

Matching training with Neural Network

I have a matching algorithm that is based on making an comparison score. This score is divided into parts. Example: 5 - Points for attributes (lets say they have 3 common attributes, would the score ...
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1answer
52 views

Neural Network Design Challenge

i'm studying for PHD Entrance Exam on Stanford. one of previous material exam designed very challenging. i want to design a NN for classifying following 2-class problem. 1) output should be -1 or ...
2
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0answers
68 views

How can you use HMMs and ANNs for on-line handwriting recognition?

On-line handwriting recognition is the task of converting a series of $(x(t),y(t))$ coordinates to symbols and words. In contrast to off-line handwriting recognition, where you only have a bitmap of ...
0
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0answers
45 views

Backpropagation Through Time Recursive Algorithm

Would it be plausible to write a recursive version of backpropagation through time for recurrent neural network training? I've only found the iterative version: ...
4
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0answers
27 views

How to choose proper activation functions for hidden and output layers of a perceptron neural network?

As far as I know choosing an activation function for the input layer is relatively straightforward: I use Sigmoid if the input data domain is (0,1) and TANH if it is (-1,1). But what activation ...
2
votes
1answer
35 views

How do I “tell” a simple perceptron/adaline neural netrork its output can't be negative?

I have made and trained a simple neural network which now seems to produce outputs reasonable in all the aspects but one: it gives negative values from time to time even though the outputs are always ...
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2answers
73 views

How to determine if a state is a fixed point in a Hopfield network?

I have been reading a lot and I am still unsure of how to determine this. Let's say I have an initial binary state vector (1, 1, 1). How would I go about determining whether (1, 1, 1) is a fixed point ...
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votes
2answers
156 views

Perceptron learning rule for classification

That's the problem $$y=(x,w,\rho) = \begin{cases} 1 & \sum_{i=1}^3 w_ix_i >\rho\\ 0 & \text{otherwise} \end{cases},$$ where $x=\{x_1,x_2,x_3\}$ are inputs, $w=\{w_1,w_2,w_3\}$ are ...
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2answers
71 views

Perceptron learning rule doesn't work [closed]

i'm a little bit lost ... can you help me ? So I have this table of date (each row give a point with its group) So i took a random weight let's say : [1, -2] H = 1 if n =< 0 0 otherwise ...
1
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0answers
14 views

What types of images should I use for negative examples in a classification problem? [duplicate]

I am doing a project to recognize a kind of leaf using ANNs with Emgu CV in C#. My project is to get frames from camera then present them to the ANN and have the ANN tell me if that frame contain a ...
1
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1answer
88 views

What are good counter-examples when training an apple classifier? [closed]

I am doing a project in order to recognize an apple. (I am using Emgucv with Visual Studio 2010 C#, if that's relevant). My project is a classification (is or is not an apple). I have 2000 images of ...
4
votes
2answers
50 views

How can an artificial neural net change the sign of a weight?

My neural net is having trouble switching the sign of a weight. The issue is that the deltas applied to the weight are proportional to that weight, so when it gets closer to zero, the deltas become ...
2
votes
2answers
59 views

What kind of model is used by 20 Questions?

Which kind of machine learning concept / model is used in 20 Questions? Is this kind of thing best solved by a neural network? Where I can read something about it?
2
votes
1answer
56 views

Neural network: noisy temporal sequence converter (transducer?producer?) on demand?

I start to suspect this problem is very hard now that I cannot find a single relevant literature on the subject, but it's too late to change the class project topics now, so I hope any pointers to a ...
3
votes
1answer
226 views

Does there exist a data compression algorithm that uses a large dataset distributed with the encoder/decoder?

If my goal were to compress say 10,000 images and I could include a dictionary or some sort of common database that the compressed data for each image would reference, could I use a large dictionary ...
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votes
1answer
51 views

In back propagation why is this necessary, o (1 - o) [duplicate]

To calculate the error in back propagation you would use, (target_output - actual_output) * actual_output * (1 - actual_output) So what does, actual_output * (1 - actual_output) solve? Wouldn't, ...
1
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1answer
45 views

What's the purpose of the “o(1-o)” in the back propagation algorithm

I'm not sure what the purpose of the o(1-o) in the back propagation algorithm achieves? I'm guessing it's related to using the sigmoid function on the output but I'd like to have a proper ...
1
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1answer
116 views

How are Bayesian Nets, Hidden Markov Chains, Conditional Random Fields and Neural Nets related?

I am having an AI exam in two weeks, and I am still figuring out certain concepts and ideas, related to Bayesian Nets, Hidden Markov Chains, Conditional Random Fields and Neural Nets (yes it is all ...
8
votes
3answers
31 views

When should I move beyond k nearest neighbour

For many machine learning projects that we do, we start with the k Nearest Neighbour classifier. This is an ideal starting classifier as we usually have sufficient time to calculate all distances and ...
3
votes
1answer
432 views

What are the key differences between Spiking Neural Network and Deep Learning

Deep Learning, now one of the most popular fields in Artificial Neural Network, has shown great promise in terms of its accuracies on data sets. How does it compare to Spiking Neural Network. Recently ...
2
votes
1answer
80 views

Neural Network Normalization and de-Normalisation of data

I am developing a simple backprop neural network with n inputs and 1 output. I am using a sigmoid activation function. [Aforge.Net] I have read that it is good to normalise the input and output data ...
0
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2answers
64 views

Support Vector Machines as Neural Nets?

This is more of a conceptual question. I have learned about Neural Nets, and I have some clue as to how Support Vector Machines work. I read somewhere however that given the appropriate kernel (is ...
1
vote
1answer
41 views

Multi-dimensional Neural Network for fingerprint matching

I want to use “Fingerprint matching using multi-dimensional ANN” by Rajesh Kumar and B.R. Deva Vikram [content link] for fingerprint identification. But I have a serious problem understanding what is ...
0
votes
1answer
99 views

How to make a Neural network understand that multiple inputs are related to the same entity?

Neural networks can have multiple inputs. But some times two or more of these inputs can often be related to a single entity. E.g : Height and weight of a person to predict the probability of disease ...
1
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1answer
89 views

Making feature vector from Gabor filters for classification

My aim is to classify types of cars (Sedans,SUV,Hatchbacks) and earlier I was using corner features for classification but it didn't work out very well so now I am trying Gabor features. code from ...
8
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1answer
966 views

What is the difference between a Neural Network, a Deep Learning System and a Deep Belief System?

What is the difference between a Neural Network, a Deep Learning System and a Deep Belief System? As I recall your basic neural network is a 3 layers kinda thing, and I have had Deep Belief Systems ...
5
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2answers
252 views

Kernelization trick, for neural networks

I've been learning about neural networks and SVMs. The tutorials I've read have emphasized how important kernelization is, for SVMs. Without a kernel function, SVMs are just a linear classifier. ...
3
votes
0answers
51 views

practical use of a Boltzmann machine

I am reading "Neural Networks and Learning Machines" and in Chapter 11 the book covers Boltzman machines and it is stated "the network [Boltzmann machine] can perform pattern completion", but does not ...