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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 summarize very large neural networks?

I am doing a lot of work with transfer learning at the moment (using keras and tensorflow if that is relevant). I am having a lot of issues in sufficiently summarizing the very large models. This post:...
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Why can't we use linear activation function in hidden layers?

I read a few articles which were stating that we need to add nonlinearity but it wasn't clear why we need nonlinearity and why can't we use linear activation function in hidden layers. kindly keep ...
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Can we combine two models in which one was implemented in tensorflow and other one in pytorch?, to see the results of 2 models simultaneously?

To further explain my question. I am implementing 2 models. 1 is for action recognition and the 2nd is for weapon recognition. If there is a situation where a person is punching or kicking someone and ...
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Is there a way to set different confidence thresholds for different classes using Detectron2?

I am working on an object detection model for microscopic images. Some of my classes are very simple (feature wise), they are practically fancy lines. Some are more complicated objects. My problem is ...
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how to tune hyperparameters inn regression neural network

hope you are enjoying good health,i am trying to built a simple neural network which has to predict a shear wave well log values from other well logs,but my model's is stuck in mean absolute error of ...
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Why does hyperparameter tuning occur on validation dataset and not at the very beginning?

Despite doing/using it a few times, I'm still slightly confused by the use of a validation set for hyper parameter tuning. As far as I can tell, I choose a model, train it on training data, assess ...
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log mel energies

I want to convert mel spectogram to log mel energies what I used is ...
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Large jumps in loss in simple transformer model?

As an exercise, I created a very simple transformer model that just sees the same simple batch of dummy data repeatedly and (one would assume) should quickly learn to fit it perfectly. And indeed, ...
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Proper datashape and model architecture for recognizing highs and lows in a chart

I am using a Keras LSTM model to try to pinpoint the highs and lows (relative high points and low points) in a chart (I need the actual coordinates to those highs and lows, not just an image). The ...
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RNN to model DNA sequencing classification

I have a DNA sequence dataset each mapped to a certain class. e,g TCAGCCGAGAGCTCATCGATCGTACGT 2 ATGCAGTGCATCGATCGATCGTAGAAC 3 Where the number after the sequence specifies the type of protein this ...
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Neural Network Stuck at Low Accuracy

I am new to deep learning so forgive me if this is an obvious mistake, I have tried to find similar questions online yet none seem relevant to my problem. I am using pytorch for image classification ...
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Deep Learning in a Camera

I don't know if this is the right place to ask this. Is it possible to run deep learning inside a camera and if so, how? I want to be able to take a picture and then use deep learning techniques to do ...
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Can I use a 1d convolution on a set of coordinates?

So i am training a reinforcement learning agent. It is fed in its position, and the target positions using x,y coordinates. An example input would be like [[1,3],[2,2],[5,1]]. I thought that since if ...
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Which model is better able to understand the difference that two sentences are talking about different things?

I'm currently working on the task of measuring semantic proximity between sentences. I use fasttext train _unsiupervised (skipgram) for this. I extract the sentence embeddings and then measure the ...
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Could Attention_mask in T5 be a float in [0,1]?

I was inspecting T5 model from hf https://huggingface.co/docs/transformers/model_doc/t5 . attention_mask is presented as ...
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How to improve the result? Should I remove the columns?

I am using this dataset, the target column is the last one which is 'DEATH_EVENT', I have separated this last one. I am using KMeans to calculate the number of hits and misses. The result is quite bad,...
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how to calculate loss function?

i hope you are doing well , i want to ask a question regarding loss function in a neural network i know that the loss function is calculated for each data point in the training set , and then the ...
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how to reduce overfitting and improve confusion matrix

I am trying to apply the following model on my data which is consists of (4030 samples as 5 classes) each sample is MFCC features which is extracted from an audio clip consisting of (20 second) and I ...
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Deep learning techniques for concept similarity?

Given a corpus of product descriptions (say, vacuum cleaners), I'm looking for a way to group the documents that are all of the same type (where a type can be ...
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How to analyse the accuracy and loss graphs of model history?

I want to understand how to analyse the loss and accuracy (any metric) graphs that we plot from the model training history. Here's my graph, What can we say from the slope of graph? Does it matter? ...
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extract features from low resolution

I have medical images and need to extract features from the layer before the classification layer using VGG for example but the ...
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Understanding nn.Conv2D in pytorch

I am trying to learn the basic of pytorch so I can assemble my own CNN's. One thing I am also trying to learn is navigating the API documentation. Specifically at the moment I am trying to read ...
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If two functions are close apart can I proof the difference of their empirical loss is also small?

I am trying to understand the proof of Theorem 3 in the paper "A Universal Law of Robustness via isoperimetry" by Bubeck and Sellke. Basically there exist atleast one $w_{L,e}$ in $\...
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Using a fine-tuned model for a different dataset

I have a dataset of different sentences from news articles which I need to classify by their sentiment. For that goal I'm planning to use a fine-tuned model which was fine-tuned on different datasets, ...
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Book recommendations for ML/DL Infrastructure Design

I'm looking for recommendations of books in the area of Infrastructure/SRE/DevOps but oriented to DL/ML. I know a few of them that seem to be too focused on production but what about training? How to ...
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WGAN-GP: how to understand whether my networks are working as they are supposed to?

I am training a WGAN-GP. Is there any way to verify whether my networks are working as they are supposed to during training? I have no feeling about the outputs of my networks. I do not want to wait ...
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How to perform crossover between models of different sizes in deep genetic algorithms?

I'm working building a genetic algorithm that will learn to play snake. I've worked out how to add/remove layers and neurons in the model, allowing the model's size to change through mutation. But ...
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In which way GAN generator transforms the data(for transforming a noise to the data)?

I have the problem: I understood how GAN works in general, but I need information how it work detailed. The part I don't understand is how the random noise at input is transformed to data on the ...
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Multi task cpu might be faster than gpu ? for classification using deep neural network

Multi core operation on CPU might be faster than GPU? For classification using deep neural networks. I focus on inference process here.
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How can deep learning be applied to association rule mining?

Association rule mining is considered to be an old technique of AI. Rules are mined on statistical support. How can deep learning be applied to this? What are approaches for structured data (in a ...
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I need to plot only training curve in the fastai library using the learner.recorder.plot_losses() function . FASTAI devs pls help

I have a task where I need to only plot the training loss and not the validation loss of the plot_losses function in the fastai library with learner object having ...
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Contextual word embeddings from pretrained word2vec vectors

I would like to create word embeddings that take context into account, so the vector of the word Jaguar [animal] would be different from the word Jaguar [car brand]. As you know, word2vec only gives ...
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How can i deal with this overfitting?

I trained my data over 40 epochs but got finally this shape. How can I deal with this problem? Please as I used 30.000 for training and 5000 for testing and ...
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ValueError: Mixed precision training with AMP or APEX (`--fp16` or `--bf16`) and half precision evaluation (`--fp16) can only be used on CUDA devices

i’m fine tuning the wav2vec-xlsr model. i’ve created a virtual env for that and i’ve installed cuda 11.0 and tensorflow-gpu==2.5.0 but it gives the following error : ValueError: Mixed precision ...
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Derivative of MSE Cost Function

The gradient descent: $\theta_{t+1}=\theta_t-a\frac{\partial}{\partial \theta_j}J(\theta)$ But specifically about $J$ cost function (Mean Squared Error) partial derivative: Consider that: $h_\theta(x)=...
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How to solve this ValueError: Dimensions must be equal

I'm trying to train an autoencoder model with colored image samples but I got this error ...
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CNN Eliminate Wrong Results

I extracted images of human faces from the videos, but the model also recorded images without faces. I wrote CNN for emotion classification. In the obvious pictures, the probability is closer to a ...
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Graph Neural Networks for Segmented Images - Which Nodes do I connect?

I'm facing an interesting problem involving medical images. We are set out to test an hypothesis if certain objects in an image affect the diagnosis of a patient. I would love to hear any comments ...
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Exploratory data analysis (EDA) on large dataset

I am working with lots of data (we have a table that produces 30 million rows daily). What is the best way to explore it (do on EDA)? Take a frictional slicing of the data randomly (100000 rows) or ...
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Why Deep Learning / Neural Networs don't achieve state of the art results in tabular data problems?

Apparently, deep learning methods don't achieve state-of-the-art results on tabular data problems [1,2]. This claim appears to be known also by Kagglers. The SOTA method looks like it is the gradient ...
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How to solve MemoryError problem

I've created and normalized my colored image dataset of 3716 sample and size 493*491 as x_train, its type is list I'm tring to convert it into numpy array as follows ...
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Binary classification from local and global feature selection

I want to train a deep leaning model, consisting of images. My question is which scenariowas chosen to train the model? scenario 1 : I train images local context on Output 1, and I train images clobal ...
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Is it possible for the (Cross Entropy) test loss to increase for a few epochs while the test accuracy also increases?

I came across the question stated in the title: When training a model with the cross-entropy loss function, is it possible for the test loss to increase for a few epochs while the test accuracy also ...
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How to optimize hyperparameters in Bert?

I am using the BERT model in order to classify stereotypes in sentences. I wanted to know if there is a way to automate the optimization of hyperparameters such as 'epochs', 'batchs' or 'learning rate'...
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Which machine learning technique can be used for predictive log analysis

I have log data with 100k records. And These parameters. It looks like this. message types can be helpful for anomaly type detection. Out of total 15 message 5 ...
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Auto encoder network

Is there any rule that we should use only deconvolution operations in decoder block of auto encoder network or we can use convolution in such way that it up-samples or mirrors the corresponding ...
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Can i use Transformer-XL for text classification task?

I want to use transformer xl for text classification tasks. But I don't know the architect model for the text classification task. I use dense layers with activation softmax for logits output from the ...
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Dealing with near duplicates using NLP

I have a dataframe like as shown below ...
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What is Typical Variation Normalization?

I was reading this paper and came across a term "Typical Variation Normalization". What does that mean intuitively and formally? Any resources I can refer to know more about it?
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Why grad cam is not showing despite of no error?

I am applying grad-cam on 3d images and I see no errors, I only can see the Scan of my original images but not the grad cam. from skimage.transform import resize ...
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