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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Neural network output [on hold]

I have my neural network trained with decimal values of output, but my app needs it to return prediction on 4 bit values. I'm always getting highest prediction on {0,1,0,0}, what should i do?
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21 views

Detecting facial texture [on hold]

I would like to extract facial texture information to feed into my neural network as an indicator of beauty. The data set I am working with is just a face framed by a black box. Each face is centered ...
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12 views

How to calculate total variability matrix?

I'm writing paper about speaker recognition using artificial neural networks and currently I'm stuck with one thing. There is a Gaussian Mixture Model (GMM) that we can use to represent speech and it ...
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21 views

How to design deep convolutional neural networks?

As I understand it, all CNNs are quite similar. They all have a convolutional layers followed by pooling and relu layers. Some have specialised layers like FlowNet and Segnet. My doubt is how should ...
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24 views

How do neural networks create results like its inputs?

I understand basic neural networks (input layer, hidden layers, output layer) and gradient descent learning. However I keep hearing about news talking about neural networks painting and making jazz ...
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1answer
19 views

Difference between SNN RL and DNN RL?

In Reinfrocement Learning (RL) in Neural Networks (NNs), I've seen two approaches to Q-learning. The first is to tile the state space with basis functions using Spiking Neural Networks (SNN) to ...
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21 views

Are the Confabulation Theories of Thaler and Hecht-Nielsen Isomorphic?

Both S. L. Thaler and R. Hecht-Nielsen have set forth neural-based theories of "confabulation" applicable to machine learning. The essential mathematics of Hecht-Nielsen is set forth in his paper ...
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1answer
33 views

Predicting next action to take to reach a final state

Does anyone know of an algorithm that could be used to determine the next action to take to reach a desired state when trained on time-series data? For example, a robot starts at a certain state, ...
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1answer
76 views

Why Isn't This Outlier Score/Reconstruction Error Not Squared?

I was looking through a paper called "AI2 : Training a big data machine to defend", and saw this (http://people.csail.mit.edu/kalyan/AI2_Paper.pdf) $score(X_{i}) = \sum_{j=1}^{p} (|X_{i} − ...
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1answer
22 views

Determining if my artificial neural network needs additional layers

I have implemented a neural network for load forecasting in Microsoft Excel. My structure is very simplistic and involves only 1 hidden layer and 4 neurons. (See picture) I trained my network with ...
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1answer
14 views

Using a combination of spatial and non-spatial inputs for convolutional neural networks

I'm working on training a game AI using deep reinforcement learning to achieve specific examples based on pixel input and some additional state information. Naturally, I'm using a convolutional ...
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13 views

How does a recurrent connection in a neural network work?

I am reading a very interesting paper on genetic algorithms which define neural networks. I am familiar with how a feedforward neural network operates, but then I came across this: Where node #4 ...
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26 views

KDD Machine Learning using K-NN Algorithm Classification Problem

I'm trying to solve a classification problem from the KDD cup archive of 2004. Details can be found here: KDD 2004 Archive I'm only dong the particle physics part. The description of dataset is as ...
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11 views

understanding kohonen self organized feature maps

I was learning self organizing feature maps the other day. I want to intuitively understand it because I'm not that good at math. But I still am not very clear about it. I can easily understand ...
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19 views

recommendation of topics before learning neural networks?

I have seen that the material for learning neural networks is huge, some books take a more practical approach, while others rely more on math and others in statistics. My question is direct what ...
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2answers
64 views

How do you determine the inputs to a neural network?

I'm looking at this tutorial on neural networks. The data that is given from the UCI study includes various attributes, such as "mean x of on pixels", "total # on pixels" etc, which are taken as input ...
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1answer
31 views

How do RNN's handle providing output with different dimension than input

It seems like an RNN has to have the ht-1 needs to be the same size as the input vector since they're being added to one another, but if you're doing something like modeling to another language or ...
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1answer
20 views

Some basic help about Artificial Neural Networks

Firstly, I'd like to state that I do not have any basis to the Neural Networks and I'd like you to recommend me very simple and understandable resources. While I have a home assignment for a short ...
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11 views

How does the k-NN method influence the relevance of the query that are more similar?

Question: 'One could argue that images that are more similar to the query are also more relevant. Discuss and explain two ways to have the k-NN method take this into account.' I thought it was ...
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1answer
43 views

Role of computational power in recent AI developments

Today Google's AI won its first game of Go against Lee Sedol, one of the best Go players on the planet. Image interpretation and self-driving cars are other recent success stories in machine learning. ...
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129 views

Traveling Salesman Problem with Neural Network

I was curious if there were any new developments in solving the traveling salesman problem using something like a Hopfield recurrent neural network. I feel like I saw something about recent research ...
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17 views

RNN giving unique outputs every time and generate a finite vector

How come RNN give unique outputs each time? If the goal of an RNN is to give the most probably completion of an input statement, why do they typically provide unique outputs if you run them for ...
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1answer
82 views

How different is the working of SNN (Spiking Neural Network) as compared to a real Neuron System in biological systems?

Assuming its one step closer to realism as compared to ANN, DNNs and other Neural Network models, what are the primary differences between a real neuron system and SNN?
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1answer
73 views

Convolutional Neural Network Feature Engineering?

I'm working through the tensorflow tutorial, and I see how you go from 28 x 28 to zero-padding and applying a 5x5x32 convolution to get 28x28x32 and max-pooling etc. What I'm confused about is the 32 ...
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47 views

Difference between Elman, Hopfield & Hemming Recurrent Neural Networks

What is the main difference between Elman, Hopfield & Hemming Recurrent Neural Networks? Python Neurolab Library examples: Elman Recurrent Neural Network Hopfield Recurrent Neural Network ...
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37 views

What are the limitations of LSTMs?

For a school project, I'm planning to compare Spiking Neural Networks (SNNs) and Long Short Term Memory (LSTMs) networks in learning a time-series. I would like to show some case where SNNs surpass ...
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110 views

what is difference between multilayer perceptron and multilayer neural network?

When do we say that a artificial neural network is a multilayer Perceptron? And when do we say that a artificial neural network is a multilayer? Is the term perceptron related to learning rule to ...
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51 views

Flaw with Cross Entropy Error in Neural Networks

I've recently been working on creating a neural network to classify handwritten digits. I implemented 1-of-N encoding such that there are the same number of output nodes as possible digits (The ...
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2answers
52 views

What restrictions apply to query and target vector encoding on fast-forward neural networks?

I'm currently studying fast-forward multi-layer neural networks with back propagation, in the book I see that all query and target vectors are binary-encoded, this makes me believe that this is the ...
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41 views

Perceptron XOR calculation

I have following perceptron for the XOR-problem. I'm do not understand the perceptron or my calculations are wrong or both. My thought process: f.e. A is true and B is false. The function I use ...
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27 views

What are the most desirable properties of a neural network? [closed]

I'm trying to compare a custom neural network architecture with other existing ones. I'm quite new to the CS field and I'm looking for desirable properties and/or applications of neural ...
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2answers
41 views

What are the advantages of online learning when training neural networks?

Stochastic gradient descent with a batch size of 1 is apparently used to learn from single examples as they arrive, but I don't understand why you would use such a small batch size instead of batching ...
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1answer
99 views

Can an artificial neural network convert from cartesian coordinates to polar coordinates?

Given cartesian coordinates $x$ and $y$ as input, can a neural network output $r$ and $\theta$, the equivalent polar coordinates? This would seem to require an approximation of the pythagorean ...
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1answer
32 views

How is the Delta Rule derived in neural networks and what is the explanation for the algebra?

I am currently trying to learn how the Delta Rule works in a neural network. So far I completely understand the concept of the Delta Rule, but the derivation doesn't make sense. My question is how is ...
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1answer
27 views

Backpropagation on a matrix of functions

As nicely described in this article, backpropagation for multi-layer perceptrons defines the error term for a neuron in terms of the partial derivative of the weights. It's traditional to represent ...
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35 views

Text data comparison

Okay lets say i have two data structures . two phone data for example containing their Name and spec ( cpu , ram , display etc ) . I want to check if these two phones are the same or not . Their names ...
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2answers
76 views

Large number of layers in a neural network?

How large can the number of layers in a neural network be? Can there be maybe 1000 layers with, say, 1000 neurons in each layer? I imagine the brain has hundreds of thousands of layers.
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1answer
27 views

Genetic Algorithm Neural Network- Inputs for evolving creatures

I made a GANN program for evolving creatures. The genes that get put into the GA for each ind are the weights that go into the neural net for each creature. The NN is a basic 1 hidden layer NN with ...
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9 views

Is it possible to retrieve element in a matrix which was trained using Neural Network?

I just studied neural networks and know the basics. Problem: A matrix is trained using neural network (by giving position of an element and the element) One should get the element at (i,j)th position ...
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1answer
43 views

How can I compare two different neural networks, from a theorical point of view?

Let's say I have a problem (i.e. Given f(x), find x) and two neural networks(i.e. feedforward and recurrent). I would like to know if one works better than the other one. I could run the twos on a ...
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1answer
1k views

Convolutional Neural Network Example in Tensorflow

I want to ask the dimension change in different convolution and max-pooling layer. I am referring to the example in TensorFlow tutorial: ...
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18 views

How might one go about building neural network used to make a numerical forecast?

I've been doing some reading on the Interwebs about neural networks, and I still can't exactly wrap my head around this idea. I get that a neural network basically replicates the human brain, and ...
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1answer
91 views

How do neural networks learn concepts?

I've been learning neural networks and some back propagation stuff, and I heard about google's Tensorflow and how it could learn things like how to carry on conversations. It got me thinking about how ...
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19 views

Multilayer perceptron, adaptive learning with momentum

So I am experimenting with different configurations of multilayer perceptrons in Matlab and my training data are extracted from images which I want to classify. -I am currently using adaptive ...
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1answer
256 views

Train a neural network to play tic tac toe using a genetic algorithm

I have an assignment for school, in which I have to build a neural network that will play tic tac toe, using genetic algorithms for training. The thing is that I am clueless on how to connect the two. ...
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3answers
130 views

How are Neural Networks made so general?

After reading this blog about Deep Neural Networks learning about selfies I'm struck by how generic the network in question is. In short: I'm thinking of trying to write something vaguely similar for ...
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1answer
57 views

Language grammar correction with supervised learning

I want to work on automatic grammar correction using machine learning (possibly using recurrent or deep neural networks). The algorithm will be supplied with both corrected and initial documents for ...
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39 views

Teaching perceptrons colors? [closed]

I am learning about artificial neural networks and I've decided to go with perceptrons. I already made a sample program that can learn based on the learning data, but when I tried to make it recognize ...
2
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10 views

What would happen if a multi-layer perceptron is insufficient to learn a function?

Suppose that something is wrong with its configuration (ie topology), for example there are too few neurons in a certain layer, or not enough layers. I have an intuition that some neurons' "delta" ...
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1answer
68 views

Approximating sign function by tanh in Multi Layer perceptrons

This is an exercise problem from the book "Learning from Data". Given $w_1$ and $\epsilon \gt 0$, find $w_2$ such that, $$\lvert \mbox{sign}(w_1^Tx_n)-\tanh(w_2^Tx_n)\rvert \le \epsilon$$ Where ...