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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Wouldn't it make more sense to give less importance to gradient far away in past in AdaGrad?

This is the update equation of a weight by AdaGrad: $$w_{new} = w_{old} - \frac{lr}{\sqrt{G_{}+E}}.G_{w_{old}}$$ Where $G$ is the sum of the gradients of the same weight at previous iterations, $E$ is ...
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522 views

Is there any single disadvantage to use GPU in deep learning?

In most cases I frequently heard that to make a deep learning experiment, it is highly recommended to use GPU. It makes the computation blazingly faster than CPU, and sounds like a magical tool (...
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Why do people prefer $(target-actual)^2$ over $|(target-actual)|$

When computing loss functions, people use $(target-actual)^2$. They sqaure it to prevent any negative loss. But we can even use $|(target-actual)|$ to prevent any negative loss. So, why do people ...
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Neural network approach to the cocktail party effect

Imagine you have 2 people at 2 different microphones but in the same room. Each microphone is going to pick up some sound from the other person. Is there a good neural network based approach to ...
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Why are we taking the square root of the gradient in Adagrad?

This is how we update weights with Adagrad: $$w_i = w_i - \frac{lr}{\sqrt{g_i+E}}$$ where, $w_i$ is the $i^{th}$ weight, $lr$ is the learning rate, $g_i$ is the gradient of the $i^{th}$ weight and $E$...
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2k views

how to use tensorflow graphs in multithread?valueerror:tensor a must be from the same graph as tensor b!

I am doing instance detect and image retrieval task by Keras and Tensorflow as backend. I plan to use multi thread to load two model, I load maskrcnn in a thread and load mobile net in another one. ...
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133 views

How to model segmentation of a sequence to similar parts?

I guess LSTM is good for sequence modeling but how would you model "clustering" with it? Meaning, the input is a sequence and the output is labels with similar properties (I have labeled data). For ...
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1answer
29 views

Based on transformer, how to improve the text generation results?

If I do not pretrain the text generation model like BART, how to improve the result based on transformer like tensor2tensor? What are the improvement ideas for transformer in text generation task?
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1answer
15 views

Tools suitable for semi-automatic video labeling?

So far I have been using labelme to label objects in videos I use for training, but it is quite time consuming. Are there good tools to help with that? I was thinking about a tool where I label ...
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4 views

Sending model to client side

I have build the model to detect the distance. So i need image of camera to be sent to my server per 100 millisecond. But If this is not cool with low internet speed. So i use js tool and able to ...
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1answer
27 views

What's the best way to detect bible verse mentions in a text?

I have a set of 10 verses from the Bible in English. I want to detect the occurrence of any of these verses in a text. What would be the best way to go about doing this? Note that verses of the Bible ...
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mathematical representation of General Regression Neural Network (GRNN)

I have little knowledge about GRNN but I read about GRNN from many sources on the internet and papers (https://doi.org/10.1109%2F72.97934). \begin{equation*} y=\frac{\sum\limits ^{n}_{K=1} y_{k} .e^{\...
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Why are we squaring $V_t$ in RMSprop?

This is the equation of SGD with momentum: $S_t = B*S_{t-1} + (1-B)*V_t$ where $B$ is $beta$ and $V_t$ is the gradient at time $t$. I understand why the formula works. But, this is the equation of RMS ...
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21 views

CNN model to predict if the shops are open or closed

I'm planning to train a model used to determine if a shop is open. Images are either shot by my students or scraped from the internet. They have manually cropped them so that only one shop is shown on ...
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Using V or K fold cross validation instead of using testing set

I've used General Regression Neural Networks (GRNN) for 100 data set to predict continuous variables , I trained the model with these data set , also the fitted model was tested using 10 V fold cross ...
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361 views
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14 views

Text classification using deep learning with removing name, place address etc

I want to prepare a deep learning model for text classification(Document classification), But in my training content many place have name, address, brand name, etc.. which will do confuse to model as ...
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6 views

How does Keras.Model() retrieve the different layers between its passed input and output layers?

I have recently been learning Keras and am trying to understand how the keras.Model function is able to interpolate the different layers between passed input and output layers. Do the input or output ...
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4answers
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Few activation functions handling various problems - neural networks

How can a few activation functions in neural networks handle so many different problems? I know some basics theory behind ANN, but I can't get what functions like the sigmoid function etc. have in ...
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1answer
59 views

Deep Reinforcement Learning - mean Q as an evaluation metric

I'm tuning a deep learning model for a learner of Space Invaders game (image below). The state is defined as relative eucledian distance between the player and the enemies + relative distance between ...
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17 views

Scraping Training data for Sentiment Analysis [closed]

I'm scraping tweets from twitter for training a real time sentiment analysis system . For the training data , since I am scraping tweets , how do I ensure I get equal number of tweets for positive , ...
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1answer
108 views

Tensorflow model works for classification but not for regression (all predictions equal the output layer bias)

I'm trying to build a model for FX prediction. It's giving some promising results for classifying each period as buy/sell/neutral. When used as a classifier, actual returns are converted to 0, 1, or ...
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2answers
53 views

Is the Cross Entropy Loss important at all, because at Backpropagation only the Softmax probability and the one hot vector are relevant?

Is the Cross Entropy Loss (CEL) important at all, because at Backpropagation (BP) only the Softmax (SM) probability and the one hot vector are relevant? When applying BP, the derivative of CEL is the ...
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1answer
29 views

EarlyStopping based on the loss

When training my CNN model, based on the random initialization of weights, i get the prediction results. In other words, with the same training and test data i get different results every time when i ...
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1answer
24 views

How to continuously train a model with a stream of new incoming data

I have limited experience with machine learning, I trained a few networks, but nothing out of the ordinary. I have the following problem but I am not quite sure how to approach it and I'm hoping to ...
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1answer
319 views

Which is the “most properly working” Bert-Ner repository

I am trying to find a repository in Github to get a Pytorch-reimplementation of the Bert model for NER task. So far, I found the following repos: https://github.com/kamalkraj/BERT-NER https://github....
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2answers
302 views

How to determine the number of the training images in Keras after data augmentaion?

I want to create a CNN model and I am using data augmentation. I want know the number of augmented images in Keras. How to determine the number of the training images in Keras after data augmentation?...
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1answer
46 views

What is used for Machine Translation besides RNN

I am doing a university report and it seems that encode-decode RNN are optimal for Machine Translation. I need something else to compare it to but I can't seem to make a proper google search for it. ...
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1answer
10 views

How to add Earlystopper in Classifier Model

I have designed the following Binary Classifier Neural Network Model for a task. I want to add an early stopper to the model so that the model stops at an epoch where it has stopped learning ...
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1answer
16 views

Back Propagation Vs Learning rate in Neuralnet Optimisation

I was doing some research on how backpropagation works? I read that, backpropagation is used to find the optimal weight of each neuron after every iteration using partial derivates and updates the ...
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1answer
33 views

computer science student - asking for some machine learning guiding (voice cloning)

So I have chosen my synopsis topic for my second last semester. I want to make a text-to-speech program, that speaks with the voice of a game character. I have worked with machine learning in my ...
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1answer
42 views

In a Time Series Problem, is it possible to forecast quantities by learning the patterns of other items? What are my options?

Suppose I own a store that sells a variety of apples and I have the following stats each month. Report Date Type of Apple (TA) Quantity Available(QA) Quantity Sold in the Past 30 days(QS30) ...
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40 views

How and why does SGD with Momentum really work? [closed]

Alright, now I am asking this. I have read dozens of articles on SGD with momentum, watched dozens of videos on it. None explained, how and why does it work, properly. I have just created an account ...
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2answers
514 views

how to calculate quadratic features in computer vision Neural Network

I am recently watching some tutorials for deep learning from Dr Andrew Ng on Youtube. Link is hereThe Youtube video There is a concept of number of features in convolutional neural network in ...
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1answer
33 views

Why is it valid to remove a constant factor from the derivative of an error function?

I was reading the book 'Make your own neural network' by Tariq Rashid. In his book, he said: (Note - He's talking about normal feed forward neural networks) The $t_k$ is the target value at node $k$, ...
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2answers
35 views

How to combine two different embeddings in the best way possible?

I have two models which are giving two books embedding Ml_model_a => book1_embedding [ 1, 200 ] Ml_model_b => book2_embedding [ 1, 200 ] I am building a ...
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33 views

Python: ValueError: Unknown layer: Functional

model_path = './models/VGG16_res.h5' model = load_model(model_path) This is the code which I'm using to load a model TensorFlow version: 2.3.0 I'm not sure ...
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18 views

Cat2Vec implementation X = categorical and y = categorical

I am trying to convert categorical values (zipcodes) with Cat2Vec into a matrix which can be used as an input shape for categorical prediction of a target with binary values. After reading several ...
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1answer
29 views

How to use 5 by 5 or 7 by 7 kernel size for a deep learning network with 3 by 3 kernels?

I am using a U-Net architecture. The visual area of the segmentation mask is very small and after learning it is giving a lot of false positives. I am thinking of ...
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2answers
362 views

DQN - how is it possible to train separate outputs for each action?

I'm trying to implement a Deep Q Network, but I'm stuck on how you train a network to predict multiple action-values when you can only collect data on a single action. In the paper it recommends using ...
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1answer
64 views

CNN 3D line angles prediction regression - results of training of phi depend on theta

I am a beginner in "deep learning". What I am trying to do, is to predict two angles of a 3D line projected on a 2D image. The toy model is that I create a line going out from the centre of 48x48 ...
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1answer
109 views

*Challenge* Making an algo that learns from a book, and can answer anything about it

I recently took this challenge where I am trying to make a set of algorithms to read any particular book, understand and store the context and subsequently answer any question asked about it. In ways ...
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2answers
535 views

Custom Named Entity Recognition using deep learning

I have a dataset with two columns. First column has some text (news article) and the second column contains names of people (not exactly English names) present in those news articles (first column). I'...
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20 views

Effect of label normalization on optimization?

Let's say in a regression task I have a range of labels 1-60. If I normalize the labels and squeeze those into 0-1 range (by dividing 60) and calculate loss then the calculated loss will be very small ...
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1answer
61 views

What are the key differences between a MLP with lagged features and a RNN

I've been working with MLP's for a while. Whenever I assumed that the past values of a feature might be useful for predicting the future values of Y, I would just create a new column in my data frame ...
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18 views

Prediction of output as 1 D vectors from multivariate time vector inputs

I have a data set where data in form of time sequences(which is evenly sampled at 1 sec). For each set I have 4 inputs and 1 output. The length of input and output vectors is same. The entire data has ...
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1answer
34 views

Cocktail Party Problem using ICA

If you already had a recording of two separate sound sources mixed, where one source was a person singing and the second source was a piano playing. Could ICA be used to separate the recording into 2 ...
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6 views

accuracy and loss NAN for keras multi-label Neural network learning [duplicate]

When I ran a Neural Network modeling for multi-class labeling using Keras, the accuracy, loss, val_accuracy, and val_loss all seems to have nan at some point or other during the training process... ...
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1answer
13 views

What is the meaning of Face Recognition in wild and in static?

What is meant by when someone says face recognition on wild dataset and on static dataset?
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1k views

Understanding Timestamps and Batchsize of Keras LSTM considering Hiddenstates and TBPTT

What I'm trying to do What I am trying to do is predicting the next data-point $x_t$ for each point in the timeseries $[x_0, x_1, x_2,...,x_T]$ in the context of a date-stream in real-time, in theory ...

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