Questions tagged [neural-network]
Artificial neural networks (ANN), are composed of 'neurons' - programming constructs that mimic the properties of biological neurons. A set of weighted connections between the neurons allows information to propagate through the network to solve artificial intelligence problems without the network designer having had a model of a real system.
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What's wrong with my implementation of an MLP?
I'm trying to predict housing prices from a Kaggle dataset using an MLP with 3 hidden layers (10 neurons each). Having read about MLPs and backprop in the CS229 notes, I tried to do my own ...
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Does using different optimizer change the loss landscape
I plot the landscape using this code, and I notice the landscape shape has changed a lot. My understanding is that the optimizer does not change the loss landscape. But now I'm confused if its just ...
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Do categorical embeddings leak data in time series?
I am a bit confused on this matter, I can't find any resources that touch on the following but my logic says that embeddings do introduce data leakage in time series:
Considering a temporal dataset ...
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How to combine a classificiation dataset with a pair-wise comparison dataset
Let's say I'm trying to train a neural network that predicts a single output [0.0, 1.0] value that correlates to photo realism which I can use either in a classification setting or for ranking. I have ...
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How do I ensure final output shape matches input shape for a semantic segmentation task?
I trying to replicate the semantic segmentation example
https://keras.io/examples/vision/oxford_pets_image_segmentation/
but train on my own data. I have 8 labels (7 features + background). My images ...
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Pytorch backward error
Here I overcame a problem about backward, below is a simple example written by python code.
the print information is that
This really confused me as self.weights is used as a important part in my ...
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deep learning for stock prediction
I am learning deep learning . Right now I am using MNIST data set, which contains tens of thousands of scanned images of handwritten digits, together with their correct classifications.
My question ...
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Training the neural network does not give the expected result
I'm trying to create a pytorch neural network capable of recognizing peaks in 2D graphs. Previously, I was able to get a result close to what I wanted, but it was not ideal and did not give a ...
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Finding invariant feature areas within representation vector for each meta-class/group?
I have pairs of images which are not the same class, but are from the same meta-class/group. I have a standard CNN which produces a representation for each sample.
If I have several pairs of images ...
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How to derive the formula 13 in the Xavier Initialization paper
How to derive the formula 13 in the Xavier Initialization paper Understanding the difficulty of training deep feedforward neural networks from the formula 6?
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Temporal mismatch
I am building a predictive model to determine risk for a disease over the course of a hospital stay. I am using medical records from a hospital electronic medical record database. The predictions are ...
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What is the best way to train a neural network with a variable number of inputs?
Suppose I have a neural network with 5 inputs: [A,B,C,D,E]
There is only 1 output. The expected accuracy of the model should increase when all 5 inputs are ...
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ML Methods For Modelling Latent Variables
I have some time series predictor variables, $\{\mathbf{X}_t\} = \{\mathbf{X}_0, \ldots, \mathbf{X}_n\}$, and some other time series data $\{\mathbf{Z}_t\} = \{\mathbf{Z}_0, \ldots, \mathbf{Z}_n\}$.
...
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Improving Detection Model - Adding image clarification
I trained an object detection model with 5K images, it works most of the time, but I am facing an issue, for few times, the object is not getting detected.
So, I planned to retrain the model, for that ...
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What are the analogies between decision trees and neural trees?
How can I draw analogies between decision trees and neural trees? For example, how are splitting thresholds analogous between these models, and how can paths in a neural tree be represented in a ...
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