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# 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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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 ...
1answer
98 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 ...
1answer
295 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 ...
2answers
105 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 ...
1answer
984 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: ...
1answer
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://...
2answers
726 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, ...
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 ...
1answer
152 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 ...
1answer
651 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 ...
1answer
162 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 -> ...
1answer
67 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 ...
1answer
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 ...
1answer
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-...
1answer
266 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 ...
1answer
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 ...
1answer
19 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 ...
1answer
40 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 ...
1answer
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 ...
1answer
47 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 ...
1answer
216 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 ...
1answer
20 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 ...
1answer
85 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-...
1answer
2k views

### 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 ...
2answers
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 ...
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 ...
1answer
38 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 ...
1answer
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. ...
1answer
108 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 ...
1answer
246 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?
1answer
417 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 ...
1answer
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 ...
1answer
64 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 ...
1answer
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 ...
1answer
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 ...
1answer
179 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 ...
1answer
195 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 ...
2answers
78 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 ...
1answer
831 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 ...
1answer
223 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 ...
1answer
524 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 ...
1answer
60 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 ...
1answer
170 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 = ...
1answer
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 ...
1answer
816 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 ...
1answer
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 ...
1answer
64 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 ...
1answer
136 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 ...
0answers
4 views

### Is there a way to connect a deep language model output to input?

In models like GPT-2, TXL and Grover, is there a good way to know which input weights (tokens) resulted in each token of the output?
0answers
18 views

### Value flow (and economics) in stacked reinforcement learning systems: agent as reinforcement environment for other agents?

There is evolving notion of stacked reinforcement learning systems, e.g. https://www.ijcai.org/proceedings/2018/0103.pdf - where one RL systems executes actions of the second RL system and it itself ...