# Questions tagged [perceptron]

An early example of neural network without any hidden layers and with a single (possibly nonlinear) output unit.

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### ADALINE simple implementation with 2 features bug

I am reading Machine Learning with PyTorch and Ski-kit learn book by Sebastian Raschka While plotting the decision boundary (a line in this case, since the number of features considered = 2) I can't ...
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### Find weights and bias of Discrete Perceptron

I am studying for an exam and came across this question which I am unable to solve. How do I find the corresponding weights and biases. I understand that it is a perceptron so the activation function ...
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### Trouble understanding why adaline works

Recently came across adaline (an improvement on perceptron) but I am having trouble understanding why adaline works. Lets take an example of 2D binary classification task. Assume line 'l' is a linear ...
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### Confusion regarding the criteria for defining a ML model as a linear model

I am confused about the criteria which determines whether a model is linear or not. As far as I understand, the following statements are equivalent : A model is linear Output class label is a linear ...
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### Is it possible to predict non-classifiable label using single layer perceptron and sigmoid function? (without using any perceptron library)

Imagine predicting BMI index like 1,2,3,4,5 and having weight and height as input. I know it can be easily done with other method. Also I have to use sigmoid function and I am really new to this. I ...
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### Why there are two different versions for batch perceptron algorithm?

In the book "Understanding Machine Learning, S. David Ben et al.", the authors describe the Batch Perceptron Algorithm as follows: However, in the book "Python Machine Learning, ...
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### Does linear separability with gamma margin guarantee convergence of perceptron algorithm?

I am studying perceptron for the first time. I came across the assumption from online resources that if the data is linearly separable with gamma margin then the perceptron algorithm will converge. Is ...
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### Proving Perceptron algorithm mistake bound is tight [closed]

How would I prove the Perception mistake bound is tight. Avrim Blum’s lecture notes claim that the upper bound for mistakes is $\frac{R}{\gamma}^2$, but I don’t understand how to prove this is mistake ...
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### Does it matter which variable I assign 1 or -1 in a perceptron machine learning algorithm

I am using perceptron machine learning to solve the binary classification problem A vs B. For this I have to assign the actual values of A and B to either 1 or -1 to be able to use perceptron. Does it ...
1 vote
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### Can Perceptron and Naive Bayes classifier create a vertical decision boundary in a two-dimensional graph?

A decision boundary like in the picture.
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### How to show that the gradient of the smoothed surrogate loss function leads to perceptron update?

This is about the contents of section 1.2.1 and 1.2.1.1 of the book "Neural Networks and Deep Learning: A Textbook". The link to the sections is here. The question arises from the following ...
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### Did Hinton introduce the concept of distributed representation?

From Goodfellow et al.'s Deep Learning book: Several key concepts arose during (...) the 1980s that remain central to today’s deep learning. One of these concepts is that of distributed ...
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### Stop criterion is Infinitive in Perceptron in Sklearn

I read code in book "Hand-on Machine Learning in Sklearn and TensorFlow" by Aurelien Geron ...
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### Why pure exponent is not used as activation function for neural networks?

The ReLU function is commonly used as an activation function in machine learning, as well, as its modifications (ELU, leaky ReLU). The overall idea of these functions is the same: before ...
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### How to resolve the perceptron dilemma for binary classification?

I have a following thought problem involving perceptron and binary classification that I wonder if anyone has thought about before. This is not from any textbook or reference, although I doubt I'm the ...
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### In AI/ML, using the Perceptron model, would it ever make sense to have both negative weights and data?

I understand the math but I want to make sure I understand the mapping back to real world scenarios. Thinking about it logically, I cannot think of a real world scenario where you would have a ...
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### In what way are SGD and the Perceptron learning algorithm very similar?

I'm reading Hands-On Machine Learning and the author states that: You may have noticed the fact that the Perceptron learning algorithm strongly resembles Stochastic Gradient Descent. In fact, Scikit-...
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### One-hot encoding in Keras for R [closed]

I am trying to build a binary classifier using a MLP with the Keras package in R. My question is, why does the package require the labels to be a one-hot vector? For example, the value 1 will be the ...
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### Best approach for energy demand forecasting

I am trying to predict the amount of energy demand(Wh) of the next two weeks per hour. The dataset I have, contains each hour of each day since 2019 of the energy demand, is something like this: ...
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### How to draw the single perceptron decision boundary when weights and bias are 0?

I've been following an algorithm described on a book called Knowledge Discovery with Support Vector Machines by Lutz H. Hamel. In the book, there is this learning algorithm for a single perceptron ...
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I've always been a bit confused when it comes to Deep Learning terminology. Is the definition of the perceptron, whether single layer or multi layer, associated with a specific type of activation ...
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### Rule of thumb for Data Requirements when Designing a Neural Network for Deep Learning

I'm designing an MLP classifier and I've been noticing that: Using a very shallow network, or one whose at least one layer has a small number of neurons yields bad performance Using a deep network ...
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### Perceptron as a Logistic Regression

If by following way single perceptron is made to work like Logistic Regression. How much correct is it to say that I made perceptron to work as Logistic Regression. Question came to mind as ...
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### Why do we use Matrix in Perceptron instead of Functions?

Matrices are good objects to store connections between dimensions/entities. However, matrix computation is often time consuming and sometimes wasteful if matrix is too sparse. Also thinking about the ...
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### Why in gated recurrent unit gates are controlled by only one layer perceptrons?

Why don't I see a GRU anywhere with more than one layer of perceptrons inside, it's pretty obvious to try to put more layers in there, but I don't see anyone doing that
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### Whats the different between Logistic regression and perceptron?

In a binary classification problem, if both logistic regression and a single preceptron uses Sigmoid function, what's the difference in classification results, since they will have the same decision ...
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