Questions tagged [deep-learning]

a new area of Machine Learning research concerned with the technologies used for learning hierarchical representations of data, mainly done with deep neural networks (i.e. networks with two or more hidden layers), but also with some sort of Probabilistic Graphical Models.

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8 views

How to use Mean squared error as the loss function on CIFAR 10

I have tried using MSE on Resnet50 for the CIFAR10, no matter how I change the output layer like dense(1, relu)/dense(1, sigmoid). The model failed to converge in the training. What is the correct way ...
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Why is val accuracy 100% within 2 epochs and incorrectly predicting new images? (1,000 images per class when training)

My CNN tensorflow model reports 100% validation accuracy within 2 epochs. But it incorrectly predicts on single new images. (It is multiclass problem. I have 3 classes). How to resolve this? Can you ...
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Train LSTM model with similar cyclic sequence

I am using keras LSTM to predict a seq2seq of 2 variables. I have test results for 50 subjects with ±20 tests per subject. the data is a 2 variable sequence with shape (101,2). as you can see, the ...
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Regressing over tiny floats with Neural Networks

I am trying to regress over very small floats - of the magnitude [1e-2, 9e-3]. They're mostly in this range. Using simple ...
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22 views

RNN/LSTM architecture for mapping one input variable to three output variables per timestep

I am trying to make a regressor that maps an timeseries with one input variable per timestep to 3 output variables per timestep. I am doing this to be able to predict the three output-variables in a ...
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Looking for multi output image datasets

I'm looking for image datasets that have multiple labels. So far I could only find one dataset of age, sex and ethnicity prediction but I'm looking for something a little less known than that one. ...
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How to define a graph in GNN? [closed]

I am new to graph neural network (GNN). Without knowing a graph in advance, how can we possibly form an adjacency matrix? Assume there are 3 nodes (vertices): A, B, & C. There are could be many ...
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Siamese netwroks - how to choose loss function?

I have read several articles about siamese netwroks, and I understand that there are 3 different types of loss functions: ...
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Difference between the architectures of semantic and instance segmentation

My question is about the difference between the architectures of semantic segmentation and instance segmentation models. So, as far as I understand, a semantic segmentation model is making pixel-wise ...
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24 views

Fake News Detection Classifier approach

I have the dataset related to any domain like sports, entertainment, politics, etc. I just want to know that the approach I am using for fake news detection is valid or not. As I do not want to use ...
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26 views

Important features for detecting malware on the network

I am trying to build a model (Machine Learning) in order to detect malicious network traffic. At first, I am trying classify network traffic as malware or benign. After predicting the malware part, I ...
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What ML model to train on when using an adaptive learning rate - the most recent or the one with the least validation loss?

I am currently implementing an adaptive learning rate for a neural network, meaning the learning rate gets reduced (e.g., halves) every time the validation error plateaus for 3 epochs (exemplary, ...
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Points to remember when embarking on an organization-wide turn to AI solutions

In our organization, we are currently in the phase of building up team, skills to automate and implement AI based solutions. So, we are very early in this AI journey. Right now, we are also working on ...
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How can I reduce overfitting in CNN model for image classification, even after data augmentation?

its my first time posting here. I'm trying to build a CNN model that identifies fruits from a dataset of apples, bananas, mixed fruits, and oranges. So far, one of the things I have done to prevent ...
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1answer
19 views

Logarithmic scale for a learning curve [closed]

I'm plotting the learning curve with Python with the following code: ...
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2answers
41 views

Is reinforcement learning analogous to stochastic gradient descent?

Not in a strict mathematical formulation sense but, would there be there any key overlapping principals for the two optimisation approaches? For example, how does $$\{x_i, y_i, \mathrm{grad}_i \}$$ (...
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In a CNN architecture, is it possible to incorporate both class weights and data augmentation?

I'd like to conduct image classification using some CNN architectures, but the problem is that my classes are imbalanced, and each class has insufficient data. To solve this situation, I have a ...
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14 views

save and load capsule network model?

Hi i'm working on the deployment of a trained capsule network model into web application and i have a problem loading the model in other .py file to make predictions. i tried get.config() and ...
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CutMix VS Mixup Data Augumentation method for end-to-end deep learning Traning

I am looking for arguments on which Data augmentation (Mixup VS CutMix) method would be preferable for Image data and Time-series classification data. As for as I know, both have the following ...
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Augmentation for sound recognition of dog barks for CNNs

I am training CNNs to recognize dog barking, and for this I would like to augment the data sets I have (~30'000 10s clips with either barks, or no-barks in them). The straight forward idea was to mix ...
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My LSTM is struck with local minima

My LSTM Accuracy is low and is the same even if I go for higher epochs. I tried varying the optimizer/changing the batch size, but it still remains the same. My data: sequence length is 300, so its ...
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Ignore Inception Model Auxiliary Loss during Inference

For inception model v1, the authors used auxiliary loss to avoid vanishing problem. So they added 2 auxiliary loss to help train their model as you see in the purple boxes below, but they did not use ...
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1answer
24 views

Would it be possible/practical to build a distributed deep learning engine by tapping into ordinary PCs' unused resources?

I started thinking about this in the context of Apple's new line of desktop CPUs with dedicated neural engines. From what I hear, these chips are quite adept at solving deep learning problems (as the ...
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What is the time length of cycles in CMAPSS dataset? [closed]

If discussing about training data of the CMAPSS dataset, then how do we know that each cycle took "this much time" for its completion? I mean 10 seconds, 1 hour, or else?
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1answer
16 views

spot/stain growth in image classification problems

I am working on a problem with images where we are monitoring development of spot in certain region of image. We are able to classify spot present(NOK) or not present(OK) successfully if initially ...
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1answer
158 views

How can I choose the best machine learning algorithms from all kinds of algorithms?

I am a beginner at data science and I’ve been learning machine learning for a while with some courses online without any help of a teacher. After I’ve got to work with some real projects on my own, I ...
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how to choose the best machine learning algorithms from all kinds of algorithms? [duplicate]

guys, I am a beginner at data science and I’ve been learning machine learning for a while with some courses online without any help of a teacher and after I’ve got to work with some real projects on ...
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Scenario specific question w.r.t Q learning and deep Q networks

I will try to be concise and understandable. I really really need help. Scenario: I have a network with few nodes and links. On each link there are some slots (#1 to #800). I generate traffic requests ...
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Handwritten Text Recognition with different char set

I was trying to understand how Handwritten Text Recognition works but here I am. I did a lot of research but still, I couldn't exactly understand how will HTR architectures work even with different ...
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27 views

Train a parametrized model to sample from a known target distribution

I wonder if there is a way to train a parametrized model to sample from a known distribution such as Gaussian. We usually don't need a model to sample from a known distribution (if we know the CDF for ...
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15 views

Tuning Batch size and Learning rate in neural net

The following MCQ question is provided in "Exam Readiness: AWS Certified Machine Learning - Specialty" document. The correct answer has been marked in the document but I am not able to ...
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10 views

Applying LSTM or Deep Neural Algorithm for Mobile sensor

I am doing a project on mobile sensor Data ,I haven't used neural networks before on this type of data The data is 20750 subsamples extracted from the 1945 collected samples provided in a single .csv ...
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Binary Classifier , when Data Points are very less and number of features are very large [closed]

I am building a Binary Classifier. There is no Real World Scenario Problem Statement, We have just given only the data set and some guidelines. Number of features : 2040 All features are in decimal ...
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66 views

Soft actor-critic reinforcement learning for 100x100 maze environment

I am doing a project which requires a soft actor-critic reinforcement learning agent to learn how to reach a goal in a 100x100 maze environment as the one below: The state space is discrete and only ...
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Code sanity check with one batch overfitting. Good value for loss?

In my current setup, I am trying to train a simple Bert model (DistilBert) for a classification task with 30 classes. As is common, I performing a quick sanity check whether my code actually properly ...
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Visualization of multidimensional trajectories data stored as an array

I have 14-dimensional trajectories data in such form (an output of data.shape): (10000, 200, 14) and it looks like this: ...
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33 views

how an autoencoder denoise an image

i am using denoising autoencoder to denoise the image in the unsupervised way.But still after implementation of the denoisng autoencoder i am unable to understand how an autoencoder network know which ...
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24 views

Would it make sense to have an output layer connected to other output layers in a NN?

I'm working with data that has multiple variables which could be predicted, nonetheless I need to predict just one that is directly correlated to all of the others. Would it make sense to have a NN ...
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1answer
37 views

How to convert horizontal bounding box coordinates to oriented bounding box coordinates

I am trying to detect oriented bounding boxes with faster rcnn for a long time, but I could not make it to do so. I aim to detect objects in the DOTA dataset. I was using built-in faster rcnn model in ...
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25 views

How forecasting with LSTM model with Keras with event-based sample data? Python

I would like to do anomaly detection on a sensor, my data is recorded according to a change in value of +/- X%. From one value to another I can have a very short or very long time difference. The ...
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1answer
551 views

How do I install CUDA GPU for Visual Studio 2022 for windows 10?

I cannot find the visual studio 2019 version and every time I try to install CUDA 11.2.2 on my laptop, It warns me about not that I haven't installed Visual Studio. I've tried installing the C++ add-...
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Reinforcement Learning applied to Optimisation Problem

Problem Statement: We are given an optimisation problem; with production centres, source airport, destination airports, transfer points and finally delivered to the customers. This is better explained ...
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12 views

Is PositionalEncoding needed for using Transformer models correctly?

I am trying to make a model that uses a Transformer to see the relationship between several data vectors but the order of the data is not relevant in this case, so ...
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25 views

How to track loss and accuracy in PyTorch?

I have made model and it is working fine for the MNIST dataset but further in the assignment it says to track loss and accuracy of the model, which I do not know how to do it. I have also written some ...
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More training data - Less Memory

I have a training dataset of images common images, there are more than 5K images in this dataset. But I have less memory in Google colab- RAM-12GB. I need to train all the images but due to less ...
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Why is PyTorch's Dataloader is not inerrable?

I am working on MNIST dataset for an assignment and it seems to be I am stuck at some point for long. I have wrote my code for LogisticRegression and when I try to train the model it is not working as ...
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15 views

Training a DeepAR time series model with monthly data. Can only train on Daily

I am training a DeepAR model (arXiv) in Jupyter Notebook. I am following this tutorial. I create a collection of time series (concat_df), as needed by the DeepAR ...
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What is the difference between shap kernel-explainer and deep-explainer

I want to use shap to explain my image classification model. I read that it is better to use shap.DeepExplainer (than ...

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