I was having a look at this awesome tutorial on single layer perceptron. I tried the implementation out and it works like charm, but I was wondering if it's there any practical use for it as is (at such a low complexity degree).
Any example?
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Let's say I have a small bitmap which contains a single digit (0..9) in hand writing.
Is it possible to detect the digit using a (two-layered) perceptron?
Are there other possibilities to detect single digits from bitmaps besides using neural nets?
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I'm developing a program to recognize a character from a image using OCR techniques. Up till now I have used a method that scanned the image, but now I have to use neural networks. Please explain what is a single layer perceptron and how to use it to train the network.
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I am learning neural networks basics.
How can I implement AND, OR and XOR logic using perceptrons in C#?
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Is there a free (preferably public-domain or BSD-like license, but GPL will do) implementation of a multi-layer perceptron anywhere on the net?
I have textbook examples but the licenses are too restrictive, and although I can just about follow the math in the Wikipedia articles I'm not confident enough of getting it right and it's hard ...
Here is my perceptron implementation in ANSI C:
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
float randomFloat()
{
srand(time(NULL));
float r = (float)rand() / (float)RAND_MAX;
return r;
}
int calculateOutput(float weights[], float x, float y)
{
float sum = x * weights[0] + y * weights[1];
return (sum >...
When designing a feed forward neural network with multiple outputs, is there a conceptual difference (other than computational efficency) between having a single network with multiple outputs, and having multiple networks, each having a single output?
Although output neurons in the same network don't affect each other "on the fly", they...
Is there existing software for discriminative reranking, such as that used by the Charniak NLP parser, Shen, Sarkar, and Och's parser or Shen and Joshi's techniques? I'd like something that I can easily adapt for my own uses, which are similar to parse reranking.
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I'm having an issue with understanding how the Perceptron algorithm works and implementing it.
cLabel = 0 #class label: corresponds directly with featureVectors and tweets
for m in range(miters):
for point in featureVectors:
margin = answers[cLabel] * self.dot_product(point, w)
if ma...
Hi,
I'm having sort of an issue trying to figure out how to tune the parameters for my perceptron algorithm so that it performs relatively well on unseen data.
I've implemented a verified working perceptron algorithm and I'd like to figure out a method by which I can tune the numbers of iterations and the learning rate of the percept...
A few simple marks for those who know the answer.
I'm doing revision for exams at the moment and one of the past questions is:
What is meant by the order of a perceptron?
I can't find any information about this in my lecture notes, and even google seems at a loss.
My guess is that the order is the number of layers in a neural net...
I was reading on perceptrons and trying to implement one in haskell. The algorithm seems to be working as far as I can test. I'm going to rewrite the code entirely at some point, but before doing so I thought of asking a few questions that have arosen while coding this.
The neuron can be trained when returning the complete neuron. let n...
For the implementation of single layer neural network, I have two data files.
In:
0.832 64.643
0.818 78.843
Out:
0 0 1
0 0 1
The above is the format of 2 data files.
The target output is "1 for a particular class that the corresponding input belongs to and "0 for the remaining 2 outputs.
The problem is as follows:
Your ...