Questions tagged [neural-network]

Artificial neural networks (ANN), are composed of 'neurons' - programming constructs that mimic the properties of biological neurons. A set of weighted connections between the neurons allows information to propagate through the network to solve artificial intelligence problems without the network designer having had a model of a real system.

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30 views

Confusion with Notation in the Book on Deep Learning by Ian Goodfellow et al

In chapter 6.1 on 'Example: Learning XOR', the bottom of page 168 mentions: The activation function $g$ is typically chosen to be a function that is applied element-wise, with $h_i = g(x^TW_{:,i}+c_i)...
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Can you make predictions based only on one hot encoded data? [closed]

I am trying to build a neural network model that predicts an order's price. However, I have only categorical data and a price column. Here is a simplified version of my data: ...
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Creating a image classification model

I am working on a dataset to classify facial expressions. Dataset has 7 classes, training images 28000 and test images 7000. I created 2 models Model1: this model has 11 layers. Initially model was ...
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Why do people prefer $(target-actual)^2$ over $|(target-actual)|$ [duplicate]

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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Best way to remove useless features when there are more than 100,000 features?

I am in a situation where i have more than 100,000 features, and i need to select the top features to give them to my final neural network model. So far i have been using RandomForestClassifier in ...
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Increase accuracy for clothes Object Detection [closed]

I work on clothes detection and recognition task. The main idea find all clothes classes in user photo and highlight its. I collected dataset, which contain 60000 - 93 classes. My realized algorithm ...
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Reducing training data to a smaller subset for neural network regression task

I have a large dataset of several billion inputs of categorical attributes. The neural network performs a regression task. Since I want to train the model, but not always use all of the data, how can ...
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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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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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Appropiate ways of treating Inf or NA ratio variables for xgboost or random forest

I have several variables ordered by date, and I would like to take the ratios of growth from one month to the next one. I understand that it is normal to loose the first value. But my question is, how ...
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How can you use a neural network to extract the needed information from social media ads? [closed]

How to solve the following problem using neural networks, artificial intelligence and machine learning? Input data - is an ad from a channel or group of a social network. For example, this: "A ...
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What ML architecture fits fixed length signal regression?

My problem is of regression type - How to estimate a fish weight using fixed length signal (80 data points) of the change in resistance when the fish swim through a gate with electrodes (basically 4 ...
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1answer
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Which machine learning model to choose? [closed]

I am a beginner in data science. I am facing the problem of choosing the most appropriate algorithm for my specific problem. I am building a recommendation system that gives students insight into ...
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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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Does forward propagation update weights?

I know back propagation updates weights based on the results of loss/error functions. But I am not sure whether forward propagation also update the weights? If it does, how?
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Representing a 2d-grid around an agent

I'm trying to train a neural network-based model to play a game similar to Pac-Man, except there's no maze. i.e., the player is in a 2-dimensional grid, with dots of food in some locations, and the ...
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1answer
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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
21 views

Why deep feedforward neural networks are restricted to solve classification problems?

I want to use deep neural networks for regression problems, but as far as I've read through, it's mainly for classification. I was wondering why can't a regular convolutional network, or a multilayer ...
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37 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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1answer
28 views

How to perform upsampling (and NOT interpolation) process theoretically modelled?

As an example, I know that sampling a signal $s$ is modelled by multiplication of s by a dirac comb, which has the effect of convolving the Fourier Transform (FT) of $s$ by the FT of the dirac comb ...
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Neural Network regression negative performance

I have a problem with the performance of a multi layer perceptron regressor (neural network) and I cannot figure out why. Task: I am trying to improve a time series prediction. I have predictions of a ...
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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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Minimum value of cross entropy cost function [migrated]

If I understand right, general cross-entropy cost function can be written as: $$c := - \sum_{i} t_{i} \log (a_i)$$ where vector $\mathbf{t}$ is 'true' discrete pdf and the vector $\mathbf{a}$ is the ...
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1answer
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How to structure the output layer of an MLP that finds the quadrant of an arbitrary point in a rectangle?

I'm trying to write a neural network that outputs the quadrant of a rectangle that an arbitrary point lies in. This rectangle has its upper left at {0, 0} and its lower right at {1, 1} (e.g. point {0....
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1answer
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How to handle images of different sizes that are smaller than the input layer of a deep learning model?

I am performing human awareness detection and have trained my model using transfer learning with MobileNetV2. This model expects a tensor of dimension [Null,224,224,3]. I have applied face detection ...
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Time duration weighted recurrent neural network

Suppose the input time-series feature is $\vec{X}=[\mathbf{x_0},\mathbf{x_1},...\mathbf{x_T}]$, where at each time step $t\in[0,...,T]$, feature $\mathbf{x_t}$ is a vector with dimension $n$. Typical ...
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1answer
32 views

As RELU is not differentiable when it touches the x-axis, doesn't it effect training?

When I read about activation functions , I read that the reason we don't use step function is because, it is non differentiable which leads to problem in gradient descent. I am a beginner in deep ...
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1answer
29 views

Ordering of standardization, pca, and/or tfidf for neural network

I have 60k rows of text data. I have tokenized it into 55k columns. I am using a neural network to classify the data but have some questions about how to order my preprocessing steps. I have too much ...
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2answers
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Regularization hyperparam tuning during training

I have an idea for a regularization-hyperparam selection method, which I haven't encountered before and can't find on Google, but I'm sure someone has already tried it and I'm wondering what are the ...
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1answer
30 views

High validation loss, high validation accuracy

I'm just getting started into the field of deep learning, and I completed my first model training using PyTorch. I decided to use a pre-build model from torchvision, more specifically the mobilenet_v2 ...
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1answer
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Using categorical and continuous variables in Deep Learning

I want to apply a MLP to some business seller data. I found that the data is a mix of both categorical and continuous features. For what I read it is not advisable to feed a neural network with both ...
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Abnormal value of ROC AUC score in Binary Classifier Model

I have developed the following model for a Binary Classifier. I have to evluate using roc_auc_score. I am getting unusual values for ...
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1answer
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In Mini Batch Gradient Descent what happens to remaining examples

Suppose my dataset has 1000 samples (X=1000) . I choose batch size of 32. As 1000 is not perfectly divisible by 32 , remainder is 8. My question is what happens to the last 8 examples. Are they ...
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1answer
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Neural network regression is not dynamic enough to predict target range?

I'm working with a CNN on a regression task, using MSE as the loss function. Here's a plot of predictions vs targets for the training set. Note: Legend is wrong. Blue = prediction vs target | Red = ...
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Architecture for sequential data

I am seeking advice on a solution I am building as a part of my data science course. I have to build a web application that solves a problem created by COVID-19. My idea - For the first stage of my ...
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1answer
70 views

How can I randomly sample the space of consistent neural networks for given data?

Suppose I have a dataset $X$ and target labels $Y$. For a fixed neural network architecture, how can I randomly and uniformly sample from the space of all possible assignments of weights such that the ...
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1answer
51 views

How to stop a neural network from regressing to the mean

I'm very familiar with neural networks for classification, but I'm trying a regression task for the first time. I'm finding that the network tends to go towards guessing a mean for the whole dataset ...
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Why is the accuracy on the test dataset very low when training a neural network on an IMU dataset?

I am trying to train an IMU (Inertial Measurement Unit) dataset. The dataset contain 6 features (3-gyro, 3-accelerometer) and 1 label column. I have build a neural network via Conv1D, LSTM and Dense ...
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Why do we use stochastic gradient descent in neural networks and what are the main ideas behind this optimization technique?

I am a student and I am studying machine learning. I am focusing on neural network, and I have seen that for a neural network, to define the optimal weights, we don't use gradient descent, but we use ...
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1answer
26 views

NN Making Poor Averaging Fit whilst LGBM Regressor Fits Perfectly

I have a simple toy dataset for which the features have been encoded using a Encoder-Decoder NN. I am using the hidden feature vector from the Encoder as the X input for training a 1-step lookahead ...
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29 views

Multilabel Classification - Overfitting?

My task is the following: To input drug combinations and output renal failure-related symptoms from the drug combinations. Both the drug combinations and renal-failure related symptoms are represented ...
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1answer
40 views

Methods to improve neural network to distinguish red from blue?

I have a lot of data where im trying to distinguish red data from blue, but all the features look like this basically (see imgur). They overlap a lot, and I can only get roc auc score of 0.65 . So ...
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20 views

What are the best models developed for image classification

For inventory detection problem, when you cut single product images from the shelf, the next step you have to do is that you should label the product and identify which category this product belongs ...
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1answer
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Debugging a simple 1-D CNN for solving a simple classification problem

I have a rather simple classification problem that I am trying to solve. Each instance in my problem is a list of 1024 bytes (each byte is represented by a digit between 0 and 255). There are 2 ...
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Q : Which metrics to use for fitting problems?

I have a basic fitting problem : 20 different features, a set of 5000 examples, 1 output. I try different architectures to find the best one, and here the problem is : how do I make my decision at the ...
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1answer
27 views

How to improve deep learning model having less data

I have trained a deep learning model for regression. The accuracy of the model is poor. I am quite new to deep learning. How can I improve it? The target variable Y ...
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2answers
35 views

100% accuracy on both train and test after feature engineering

The original dataset is of ~17K compound structures almost equally divided with labels indicating yes or no, after heavy use of mol2vec and rdkit I have created ~300 datapoints Using the boosted trees ...
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Trying to use CBOW for tweet classification

I'm trying to use the Continuous Bag Of Words method for word embedding on a corpus of 7503 tweets. In particular, I'm trying to use CBOW on this Kaggle competition, which involves classifying tweets ...
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9 views

Pytorch DNN feature importance / mean decrese accuracy

I have been working on a classification DNN for metabolite data. I would like to implement a feature importance calculation to the DNN, and I saw a paper that used mean decrease accuracy for this. ...

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