Questions tagged [neural-network]
In machine learning and cognitive science, neural networks are a family of statistical learning models inspired by biological neural networks and are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown.
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Graph Neural Network (GNN) (2)
This is an implementation of a graph neural network.
Edges are represented by an egde-list.
...
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Graph Neural Nwteork (GNN) (1)
The given datasets are graph data structure that represents social interactions.
The nodes will be represented as People{node_id, edge, gender, occupation} and the ...
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Neural network text classifier
I wrote a simple NN text classifier to help me quickly sort through the new daily submissions to the arXiv. It
downloads the new submissions, processings their titles and abstracts,
trains a NN on ...
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Feature-subset-selection using autoencoder [closed]
The following listing performs feature-subset-selection (not feature extraction) using an autoencoder.
My aim is to select the best features from the 1000+ available features in the given dataset.
I ...
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Feature subset selection using neural network
This listing selects the best features from the 1011 available columns in a given dataset.
The first three columns are dropped because they are useless data.
The dataset is huge. So, they were read in ...
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One-layer linear neural network to solve a regression problem in PyTorch
Good morning everyone,
I am trying to figure out how deep learning works. My approach is mainly theoretical but I have decided to code a few deep learning projects to get a better feel of the kind of ...
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Custom neural network implementation in TensorFlow to compare normalisation vs. no normalisation on data
I am performing a sports prediction multi-class classification problem, and wanted to compare the differences in model performance between normalised and non-normalised data. You can see the 2 ...
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Simple Feed Forward Neural Network with no backpropagation yet
The following code is code I wrote in c for a simple neural network with no backpropagation implementation yet. All the header files were put in one file because I am using visual studio which does ...
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A simple word embedder only using jax
How can this code be improved? I'm a novice programmer trying to learn ml by doing it from scratch. This code is part of a transformer model that I'm working on. Do you have any ideas about how to ...
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Convolutional Neural Network (CNN) in Julia
I wrote an n-dimensional convolutional neural network from scratch in Julia (check out the GitLab repo or the GitHub repo). It implements the following layer types:
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Running a neural network backwards
I've read this paper where they basically run a network backwards.
And decided to try. Luckily, many useful functions are implemented already in pytorch.
The main idea is this:
Write a convnet
Write ...
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Recurrent Neural Network loss is NAN
I am training a neural network to use approximately 600 features (4103rd to last column of a df) to predict approximately 4000 values (7th to 4102nd column of the same df). I have standardized the ...
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Feed forward neural network
I have made a basic neural network in python. The idea is the neural network can have any structure you want, not just the standard layers where every neuron is connected to every neuron in the next ...
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ANN with Backpropagation for MINST data set
I am learning about ANN and tried it for the MINST data sets. Now I am supposted to create a neural network (ANN) with backpropagation.
The structure for the neural network I have is this the input ...
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Neural Network in Julia (Multilayer Perceptron)
I wrote a simple multilayer perceptron in Julia, which seems to work fine on different datasets, e.g. the MNIST dataset with a success rate of about 90% after a few seconds of training. But I would ...