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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How to determine if my data split is appropriate for my data size?

I currently have a model that has a pretty large dataset (50ishMB) and was performing pretty well with a 80:20 split. However, when I tried changing it up to a 50:50 split, the model performed 28% ...
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Exact Predictions for Regression problems in Machine Learning

I am working on shipment days delivery problem , where i want to predict shipment days (continuous variable target) I have tries both Neural Network and Random Forest regressors ,i got very low error ...
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Proper loss function for regression with uniform target distribution [closed]

I'm doing some simulations and I would like to estimate a real number that is uniformly distributed between minValue and maxValue...
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Deep Neural Network (ANN)

How do I improve the accuracy of an ANN model ? It is currently giving me an accuracy of 93%. ...
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LLE and ISOMAP, strange question and intrinsic low dimensional structure?

I prepare for PhD entrance exam on AI and one question is surprized me. which of the following techniques using intrinsic low-dimensional structure detection for dimension reduction? A) ISOMAP B) ...
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quereies related to autoencoder

i want to design an deep auto encoder after following keras tutorial. Input is a simple 2-dimensional image consists of 512 rows and 50 columns matrix My trial code is ...
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Issue with tensors possibly being read in. IndexError: list index out of range

I'm trying to use a pre-defined tensor flow dataset. I have read the file in but I am having difficulty with building a CNN when I split the data into x and y (or image and label data. The relevant ...
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Increasing training accuracy of U-Net segmentation model

I was working with segmentation using u-net and MobileNet. While I trained with input size 256*256 it had an output with Val loss: 0.044 (In this time dense layer was 256, 128, 64, 32 with a learning ...
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Data Anonymization for all domains?

I am using a dataset from Marketing and sales department. The dataset contains customer name (company name), company address, pincode, no of orders placed, revenue generated from that customer etc. My ...
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Which AI algorithm is best for chess?

I'm working on my chess bot, and I would like to implement simple artificial intelligence for it. I'm new in it, so I'm unsure how to do it specifically on chess. I heard about Q-learning, Supervised/...
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1answer
29 views

Yolov5 image detection without segmentation?

I have read a number of papers on Yolov5 images detection techniques. But the papers don't refers to any segmentation step done by Yolov5. While I know that it is not possible to do image ...
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1answer
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How can I measure time and memory complexity for a deep learning model?

How can I measure or find the time complexity and memory complexity for a model like VGG16 or Resnet50? Also, will it be different from one machine to another like using GTX GPU or RTX GPU?
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Can I send images + boundingboxes(as features) to an LSTM? How?

I have previously trained a YOLO v4 object detection model and I am looking to leverage the results(Bboxes) of this model and create another model to recognize/classify accidents in CCTV footage/video(...
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An efficient way to encode & embed tabular data of a video into a transformer?

So a little bit of a background: I have a folder which contains video files of lets say humans doing a certain action (i.e. walking) where each .2 seconds is documented in a ...
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ImageDataGenerator for image preprocessing

I'm trying to do a superresolution network, but I am having trouble importing my own data. I have two types of images: resized images (smaller), original images. The first one is going to be used as ...
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how a deep learning network works without using library [closed]

can anybody please provide me the solutions in layman language, how a deep learning network works. ...
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15 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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24 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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0answers
9 views

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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27 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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1answer
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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
20 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
44 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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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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7 views

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
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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
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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
162 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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1answer
16 views

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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0answers
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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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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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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