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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12 views

How to decide whether to use categorical embeddings in a neural network?

I have a binary classification task with a whole slew of binary categorical features, one multiclass categorical and a few continuous features. I initially treated the categorical features using one-...
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What's the best way to store and then call 3 Million+ records for training?

Good afternoon all, I am working on a Deep Learning project where I'm proceeding in batches (rather than a continuous pipeline). Essentially I've built a function that can convert raw data into ...
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Need for doing grad.zero_() after setting torch.no_grad() in pytorch

In the following lines of code, ...
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93 views

How to tune learning rate with HParams Dashboard on Tensorflow?

In Tensorflow documentation, it is shown how to tune several hyperparameters but not the learning rate.I have searched how to tune learning rate using HParams dashboard but could not find much. The ...
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Data points are highly overlapped and do not follow smoothness rule assumption

I am working on a very high dimensional categorical features based data set. There are two output classes and 2-dimensional PCA plot suggests that the data points belonging to both +ve and -ve classes ...
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Is this Tensorflow 1.x network get trained?

I've just started to learn Tensorflow 2.1.0 with Keras 2.3.1 and Python 3.7.7. I have found the following code from this "Omniglot Character Set Classification Using Prototypical Network": ...
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Can I used a pre-trained auto encoder as an embedding layer in the model?

The dataset has an ordinal target variable [0,1,2]. Each observation in the dataset has multiple time series. In addition, each observation have various tabular features. The purpose is to build a ...
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Using Keras fit_generator for functional keras models and custom dataset

I have to fit a model that takes three discrete inputs and produces two discrete outputs using a generator made as follows: ...
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Splitting a 10 year long time-series into multiple year time-series on Deep Learning Models

I'm using recent Deep Learning models for time series analysis such as DeepAR[1] and DeepFactors[2] for my masters. My target time series was given to me by a cement factory, 10 years of compositions ...
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Batch Normalization as input layer to learn an optimal scaling?

We all know that Batch Normalization reduces "Internal Covariance Shift" and therefore helps Neural Networks to train faster (Batch Normalization: Accelerating Deep Network Training by Reducing ...
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How to deal with images with textual noise?

I have a dataset of images collected from google and bing images (scraped). basically I want to classify these images into binary classes (positive, negative). Images that contain a text originally ...
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How good is batch normalization in avoiding vanishing/exploding gradients?

Incase of deep neural network with many layers, how good is batch normalization in avoiding vanishing/exploding gradients. Consider the problem is only due to too many layers(too many multiplications ...
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Using word embeddings for kaggle?

Not sure, if this is the right forum so redirect me if it wrong. I have started on an NLP problem in kaggle. There i have word embeddings from google news, wiki, glove in a zipped folder. I want to ...
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Xavier initialisation vs He initialisation

After reading the famous paper, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, I understand two things:- He initilization borrows on the benefits of ...
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How to estimate the OCR accuracy

I am building a system which uses ocr to extract text but i have no way to flag that information on how correct it can be and if the information needs to be discarded by just looking at the image and ...
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42 views

How to use fine tuning of BERT when i have unlabelled dataset of text documents?

I have gained a basic understanding of using BERT for various NLP/text mining tasks. When it comes to fine-tuning of BERT, I always see that fine-tuning is performed using some classification tasks. ...
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114 views

Data augmentation for multiple output heads in Keras

I have a transfer learning based two output classification problem. So, accordingly, I have formatted my data to have X_train as a ...
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Approaches For Recommender System Using Complicated Novel Dataset

I have a question about the best approach(s) I should take in building a recommender system for a project I'm working on. I have created a dataset. The dataset has the following: 400,000 users For ...
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Skip gram model on multiple sequence

From different examples, I have seen that getting an embedding from skip-gram model on a single sequence or a single corpus. However, if I have multiple sequences of same word or phases. How can I use ...
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How exactly the hidden state works in an RNN ? How to decide on how many past instances to consider?

I am unable to grasp the working of RNN because in different tutorials, it is explained differently. Please correct me as I have considered that: In a ...
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Implementing training in PyTorch

I wish to accomplish the following task in PyTorch- I have the COCO dataset, wherein each data sample is used in training YOLO v3. After being processed by the model, the sample is to be deleted if ...
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Low GPU utilisation and High GPU memory

I am training a conv net for classifying 3 classes of images of size 512,512 using Pytorch framework. I have 3 Tesla V100s(16 Gb). My GPUs utilization is really low - <10% and GPU memory is really ...
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41 views

BPR TripletLoss Recommender System

I am trying to modify the code of this repo to build a recommender system based on BPR triplet loss. In particular I modified the TripletLoss layer class like this ...
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I need some guidance how to develop myself in deep learning [closed]

Few months ago I've taken an Andrew Ng course at coursera and started my data science/machine learning journey. During those months, I've take another courses from the internet - about Deep Learning, ...
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What does anneal mean in the context of machine learning?

An article released by Open AI gives an overview of how Open AI Five works. There is a paragraph in the article stating: Our agent is trained to maximize the exponentially decayed sum of future ...
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Tensorflow keras fit - accuracy and loss both increasing drastically

ubuntu - 20.04 tensorflow 2.2 dataset used = MNIST I am testing tensorflow and i notice that validation sparse_categorical_accuracy (accuracy) and validation <...
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Untrained Network Giving 80% Accuracy

I have a two class classification problem and my neural network prior to training predicts with an accuracy of 80%. After training i have an accuracy of 75%. Can you tell me how this is possible? ...
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Image Classification low accuracy

I have a dataset that has two folders for training and testing. I am trying to determine whether a patient has an eye disease or not. However, the images I have are hard to work with. I've ran this ...
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Generate text using user-supplied keywords

I've got a use case where I need to generate sentences based on a set of user supplied keywords. Here is an example of what I need: User input: End-User: Data Scientists Region: Middle East ...
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Robustness of hyperparameter tuning

I use a Bayesian hyperparameter (HP) optimization approach (BOHB) to tune a deep learning model. However, the resulting model is not robust when repeatedly applied to the same data. I know, I could ...
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How to use multiple text features for NLP classifier?

I am trying to build text classifier, Usually, we have one text column and ground truth. But I am working on a problem where dataset contains many text features. I am exploring different ways how to ...
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1answer
17 views

How do I build a DQN which selects the correct objects in an environment based on the environment state?

I have an environment with 4 objects in it. All of these objects can either be selected or not selected. So the actions taken by my DQN should look like - ...
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Loss function for non-uniform distribution in pixel regression?

Goal: Given RGB images (x,y,3) and a grayscale heatmap (x,y,1), predict the heatmap using the RGB image as input to a neural network implemented inside Keras. Approach: Multiply heatmap by (1./255) ...
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Autoencoder to encode features/categories of data

My question is regarding the use of autoencoders (in PyTorch). I have a tabular dataset with a categorical feature that has 10 different categories. Names of these categories are quite different - ...
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Genetic algorithm - Feature selection packages in Python

Can you share some packages in Python which are implemented that I can use for selecting features based on a genetic algorithm? I did refer to this AUTO-ML post and found out that it is useful but ...
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How many computations in a CNN

I have not been able to find an answer, so if it is out there, please let me know. I would like to calculate the amount of time, that a uC needs to give me an Output of an CNN. Therefore, I would ...
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How I can train BiLSTM model with CNN for semantic similarity?

I'm build a Deep Leaning model with BiLSTM and CNN for two text's semantic similarity. My data set is format as : [s1,s2,is_similarity] with is_similarity is from 0.00 to 5.00. I want to create a set ...
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Transformer decoder output - how is it linear?

I'm not quite sure how's the decoder output is flattened into a single vector. As from my understanding, if we input the encoder with a length N sentence, it's output is N x units (e.g. N x 1000), and ...
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Dimensions of the window in Sliding Windows Algorithm

So basically I have been citing the Deep Learning Specialization Course on Coursera offered by Andrew Ng. While explaining the Sliding Windows Algorithm in course 4, i wasn't really sure how the slide ...
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NN training with repetitive features

I posted the question also on ai.stackexchange but it didn't get any answers so I though I could try here. Here is a copy paste: Let's say you are training a NN in a RL setting where the state (i.e. ...
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Identifying poker card value and suit using iOS live-capture

I'm working on a hobby project that requires me to be able to use a front facing camera to effectively scan a poker card, identifying the value and suit, using live-capture. Think dealing cards to ...
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Why did we label optical flow datasets with dense flow fields?

In optical flow datasets like Chairs or Sintel the ground truth is always a dense opticalFlow field. Why don't we have grounds for a per-block motion vector field?
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Understanding the implementation of domain adaptation algorithm

I'm trying to implement domain adaptation using stochastic neighborhood embedding based on this article. I have different input shapes in target and source domain and using 2 parallel CNNs for ...
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54 views

Replace human judgements with mathematical approach/theories

I would like to give a context of what I did. 1) Let's say there are two dictionaries (dict A and dict B) each containing a list of words/terms as shown below. 2) Now my task is to find matching ...
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Should I apply 1D or 2D CNN on binary text classification?

I am trying to train a text classification model. For all sentence examples, I limit them up to 32 words, and if there are not exist 32 words, I am creating zero pad arrays. To convert each word to ...
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37 views

How does graph classification work with graph neural networks

I am reading the paper The Graph Neural Network Model by Scarselli et al. I understand how node classification works. I am having trouble understanding how graph classification works however. In ...
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Is it a good idea to disable or strongly regularize in time series deep learning models?

I'm training a recurrent network on a stock price time series. As you can imagine, the price increases with time. I think the importance of the bias decreases as the stock increases, especially since ...
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Recommended papers on Deep Ordinal Regression?

Can people please recommend papers on Deep Ordinal Regression? I'm looking for both the basic papers and the current important state-of-the-art ones - basically, everything that one "must" know in ...
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How to reduce the detection time in MaskRCNN

I've trained MaskRCNN in GPU instance and using the saved weight, I detected it in CPU instance based system. The detection time is taking too long. ...
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Federated learning - share of ROI

I am reading about federated learning and have a quick question 1) I know in federated learning, the model updates are shared to a central server 2) All the parties involved in FL can generate ...

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