Questions tagged [neural-networks]

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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1k views

How one epoch completes in Perceptron?

I am confusing on completing one epoch, I am using Single Layer Feed Forward neural Network approach. Lets suppose i have a data of OR Gate: ...
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39 views

Crafting a dataset to train a NN that recognizes image styles

In this paper the authors show how to use two NN that respectively recognize an art style and an image content to apply style filters to photographs. However, I am confused about how would one train ...
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272 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 ...
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102 views

(OCR ) How to Recognise Handwritten fractional numbers using Neural networks

I want to be able to recognise handwritten math numbers using images of the numbers , i was able to do create a ANN model for recognising simple decimal numbers , but i have no idea on how to ...
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367 views

Can we use Convolutional Neural Network for dataset containing numeric data? [closed]

I am working on a project of " Fraud detection using deep learning" . For that I have a dataset containing some numerical attributes . Now the task is to use CNN for the above purpose. Please guide me ...
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117 views

Is there a difference between computational graphs and neural networks?

According to this post, "neural networks are a special form [of a computational graph]". I think that one can infer from that that all neural networks are computational graphs. My question then would ...
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1k views

Can CNN be used for numeric data

I have a numpy array of shape N_Samples x 360, as an example, consider the following: ...
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50 views

4 Neurons to Decide 10 Digits

I am trying to solve this algorithm exercises (http://neuralnetworksanddeeplearning.com/chap1.html#exercise_513527) in Michael Nielson's online book: Neural Networks and Deep Learning (http://...
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734 views

Scoring metric for machine learning method

For a machine learning method X (Deep Neural Nets variant), which performs classification tasks. In the output layer, for every label method, ...
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2answers
187 views

How to use Neural Network classification if data not same size?

I have data like this. [0 1 0 1 0] [0 1 0 1 0 1 1] [0 1 0 1 ] [0 1 0 1 0 1 1 1 1 0] ... I want to classify with Neural Network but my data different size . I can ...
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161 views

Why is the inner product of -1,+1 binary variables at most $n-2$ and not at most $n-1$?

In short, if $x \neq u_i \in \{\pm1 \}^n$ then why is: $$ \langle x, u_i \rangle \leq n-2 $$ but not: $$ \langle x, u_i \rangle \leq n-1 $$ ? To add context: I was reading understanding machine ...
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681 views

Why is backpropagation called backwards propagation of error, when it back propagates error derivatives?

Wikipedia says: The backward propagation of errors or backpropagation, is a common method of training artificial neural networks and used in conjunction with an optimization method such as gradient ...
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163 views

Basic queries for Object Detection using deep learning

I have recently started working on the Object detection using deep learning. I have understood the basic steps of object detection are: Input Image -> Object proposals -> Feature Extraction -> ...
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68 views

Multi modal learning from separate modal datasets

Let's say I'm trying to learn emotion given multi-modal sources. In this case, video and audio. However, I only have one dataset for video emotions and one dataset for audio. Is it possible to train a ...
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284 views

Which neural network topology is the most efficient to generate randomly shaped letters?

I have created some unique shapes, so-called "letters" for a custom alphabet, all of which can fit into 9x9 pixels. Instead of drawing countless more, I try to combine two solutions I saw in a ...
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51 views

Optimizing neural network on small training set

I'm in the process of optimizing my neural network. I'd like to optimize on a small training set (1000 rows) as opposed to my full training set (100K rows) for speed reasons. Will the optimal hyper-...
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269 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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49 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 ...
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27 views

Neural network for PDE: Should we train the PDE using more initial and boundary data at the beginning?

I was trying to solve a partial differential equation (PDE) using a neural network. The solution to the PDE is not unique unless the boundary condition is determined. In my case, the neural network ...
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41 views

What is the most efficient way to test whether a set $X \subset \{0, 1\}^n$ and its complement $\{0, 1\}^n \setminus X$ are linearly separable?

I am interested in algorithms that have optimal running time, and ideally which are also very easy to implement. If you can also give some tips on how to implement the algorithm(s) you mention in the ...
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23 views

List of all possible reasoning tasks - satisfiability and theorem proving only?

What is the exhaustive list of reasoning tasks? As far as I can understand, then any logical reasoning reduces to 2 tasks only: 1) satisfiability problem (finding the assignment of the variables) and ...
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57 views

Learning a perceptron from stream data

I want to train a Perceptron using stochastic gradient rulefrom the stream data. I have very limited amount of memory and i can store only $N$ examples. Suppose my population consist of point as ...
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1answer
50 views

Linear Regression using a Neural Network

I am trying to create a regression model using a Neural Network. I am currently learning how to work with neural networks (deeplearning.ai) and so the model is not implemented using any existing ...
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1answer
250 views

Is deep learning appropriate to approximate dynamic programming problems?

I have a problem which can be completely solved using dynamic programming, but in a very intractable way (On^4, where n is around 1000). I won't get into the details of the problem since it's a bit ...
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22 views

CNN/Neural Network: Can I still estimate 3 parameters if my input data has insufficient parameter labels?

I am trying to simplify a CNN model. Currently, I need to train 3 different models (with the same architecture) to estimate each parameter. I am just wondering if there is a way to just train one ...
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100 views

How does pre-training help with semantic segmentation with U-net

I am working with a u-net, a variation of the more commonly known fully convolutional network for semantic segmentation. For training a u-net, I was given the suggestion that I should use a pre-...
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ResNet output dimensions

So I am currently going over the ResNet paper and trying to understand the output dimensions of each of the layer, and it seems that I am already stuck on the first layer and its output dimension. If ...
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1k views

Neural Network Shape / Structure

I am attempting to learn neural networks using the Keras libary on the MNIST hand written digit dataset, using dense layers only. I am trying to figure out what the best shape for the network should ...
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1answer
43 views

References for the computational complexity of training neural networks

I'm looking for a good review paper or book chapter that offers an accessible introduction to the computational complexity of training neural networks for classification problems. In particular, I'm ...
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40 views

Are neural networks inherently imperfect because they mimic an imperfect machine (human)?

Neural networks are the base for all(most?) of the machine learning / deep learning algorithms/programs. Humans don't have fixed algorithms to decide or do something. Initially, we don't know how a ...
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46 views

Reducibility and Artificial Neural Networks

I have read (here and here ) about the computational power of neural networks and a doubt came up. There is a way to reduce an ANN to another ANN (not taking into count the training algorithm) ? e.g. ...
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112 views

Viable use of genetic algorithms to train neural nets in a poker bot?

  I am designing a bot to play Texas Hold'Em Poker on tables of up to ten players, and the design includes a few feed forward neural networks (FFNN). These neural nets each have 8 to 12 ...
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252 views

What is branch factor for beam search in RNNs like TensorFlow's Magenta?

TensorFlow's Magenta melody generation module has a parameter for branch_factor1. What is it?
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424 views

Neural Networks: Simulate the working of Dynamic Fixed Point representation of the weights on hardware

I am looking to implement a neural network on hardware using Verilog. I have completed and tested with floating point representation and a 20 bit fixed point representation. I want to further reduce ...
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107 views

Algorithm Selection for Classification problem

I've been working on a developing a product selection network for my workplace. I work with lots of chemicals and the clients don't always know what they want/need so most of the time I have to ask a ...
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1answer
65 views

Is learned RNN still universal?

I know it has been shown that RNNs are Turing complete. So for any Turing machine, there exists a configuration of a RNN that is equivalent to it. But I'm wondering, is all those configuration ...
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121 views

Why is low-dimensional discrete representation of input space useful?

Self-organizing maps undergo unsupervised training to produce a low-dimensional, discrete representation of the input space. Why is this useful? I think the results of the self-organizing map would ...
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96 views

Convolutional Neural Networks - Where should I start from?

I will soon be starting a part-time job within a start-up and I've been assigned to a project where I'll have to build a CNN that recognizes and counts people in various kinds of videos. My CEO knows ...
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180 views

Back propagation in neural networks

I just finished watching these 3 Coursera videos on back propagation in neural networks. I get the idea of what we're trying to do, but I don't get how we achieve that by calculating error in each ...
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198 views

When a trained RNN is tested, is the number of time-steps same for every input?

I am a beginner in deep learning so bear with me. If I want to unfold the RNN in order to represent the relation of output to the input as a non-recursive functions I would have to know the number of ...
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2answers
79 views

Best practices for normalizing up training, validation, and test sets

I was reading up on how to normalize my training, validation, and test sets for a neural network, when I read this snippet: An important point to make about the preprocessing is that any ...
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867 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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231 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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536 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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61 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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171 views

Creating a single layer perceptron for the OR problem

I am working on the following problem Find the linear least squares unit weights for the `OR' problem, ie. $v_1^T = (0,0), v_2^T = (1,0), v_3^T = (0,1), v_4^T = (1,1)$ and $u_1 = 0, u_2 = u_3 = u_4 = ...
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80 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 ...
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845 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 ...
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50 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
65 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 ...