Questions tagged [machine-learning]

For questions related to machine learning (ML), which is a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty (such as planning how to collect more data). ML is usually divided into supervised, unsupervised and reinforcement learning. Deep learning is a subfield of ML that uses deep artificial neural networks.

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Extracting specific information from an Invoice images

Tried to extract only specific information from the images but Couldn't, We have to automate this process using this as the format of the Images keeps on changing. LinkSample data What I have Tried: <...
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How do LSTMs work if the following two matrices are not able to be multiplied?

In the above diagram, the shape of some of the matrices can be seen in the yellow highlight. For instance: The hidden state at timestep t-1 ($h_{t-1}$) has shape $(na, m)$ The input data at timestep t ...
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how to handle highly imbalanced multilabel classification?

I am working on a multilabel classification in which I am having 206 labels. When I saw the percentage of the number of 1's in each label they are way less than 0.1% for each label. The maximum ...
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Which Reinforcement learning model to use?

Suppose we are training an environment with 2 collaborative agents with Reinforcement Learning. We define the following example: There is a midfielder and a striker. The midfielder's reward depends on ...
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1answer
61 views

Why does the training time of SVMs dramatically decrease after applying dimensionality reduction to the features?

Training an SVM with an RBF kernel model with c = 5.5 and gamma = 1.06, for a 5-class classification problem on the NSL-KDD ...
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Why do we have a sigmoid function in the input layer in LSTMs? [closed]

I'm particularly confused about the sigmoid function in the forget and input layer. If we use a sigmoid in the forget layer to look at $h_{t-1}$ and $x_{t}$, and output a number between 0 and 1 for ...
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1answer
59 views

What is the difference between neural networks and other ways of curve fitting?

For simplicity, let's assume we want to solve a regression problem, where we have one independent variable and one dependent variable, which we want to predict. Let's also assume that there is a ...
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1answer
38 views

Should the range and initial values of weights and biases be adjusted to fit input and output data?

As a routine (in typical everyday tasks) of a data scientist, should they usually decide about weights and biases range and initial values as a function of which data they are planning to insert as ...
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Bigger models get higher losses

I'm training a model with the transformer encoder architecture on a given fixed set of data. The task I'm solving has a trivial approximation which consists in copying part of the input to the output, ...
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Computer vision - Can you put more weight on a specific part of the object?

Let's say I'm looking for any item that has a certain shape (outline) in a photo. but I can further classify it only according to particular features, that most of them are expected to be shown only ...
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Could someone explain to me what each parameter of XGBClassifier means? [closed]

These are the parameters: ...
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Context of a code/program [closed]

I am working on code summarization. My model takes the code sequence to train the attention-based encoder-decoder model. However, I am wondering if it's possible to extract some kind of context from ...
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How does back-propagation through time work for optimizing the weights of a bi-directional RNN?

I am aware that back-propagation through time is used for training the recurrent neural network. But I am not able to understand how this happens for the bi-directional versions of the recurrent ...
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What is the difference between text-based image retrieval and natural language object retrieval?

I'm working on creating a model that locates the object in the scene (2D image or 3D scene) using a natural language query. I came across this paper on natural language object retrieval, which ...
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Why do we use a Softmax regression? [closed]

Could someone explain to me why, with examples?
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What exactly is an interpretable machine learning model?

From this page in Interpretable-ml book and this article on Analytics Vidhya, it means to know what has happened inside an ML model to arrive at the result/prediction/conclusion. In linear regression, ...
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Would performance of atomic models matter in ensemble methods?

Suppose I have two fitted ensemble models $F_1 := (f_1, f_2, f_3, \cdots f_n)$ and $G_1 := (g_1, g_2, g_3, \cdots g_n)$. And there were using the same ensemble methods (boosting or bagging). And I am ...
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ML Algorithm for getting top pick in each sample

I have a dataset of streets - and each street contains several houses. ...
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Why is automated theorem proving so hard? [duplicate]

The problem of automated theorem proving (ATP) seems to be very similar to playing board games (e.g. chess, go, etc.): it can also be naturally stated as a problem of a decision tree traversal. ...
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How does dimensionality reduction occur in Self organizing Map (SOM)?

We have n dimension input for SOM and the output 2-D clusters. How does it happen?
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Machine Learning Techniques for Objects Location/Orientation in Images

what Machine Learning tool can understand in which location and orientation a picture was taken from? That is from pictures of similar objects, say for example pictures of car interiors. So given a ...
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1answer
51 views

speech comment detection by deep speech mozilla for data set

I want to create a system so that when a human being says a word or command through a microphone, such as "shut down", the system can execute that command "shut down". I used the ...
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1answer
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What's the difference between estimation and approximation error?

I'm unable to find online, or understand from context - the difference between estimation error and approximation error in the context of machine learning (and, specifically, reinforcement learning). ...
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Is stable learning preferable to jumps in accuracy/loss

A stable/smooth learning validation curve often seems to keep improving over more epochs than an unstable learning curve. My intuition is that dropping the learning rate and increasing the patience of ...
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Meta-data for 3D games

With 2D and Atari games developers/researchers can use reward functions to guide the learning of the NN. Typically, the reward is tied to a score, so the NN eventually learns to do things to score ...
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What is meant by “ground truth” in the context AI?

What does "ground truth" mean in the context of AI especially in the context of machine learning? I am a little confused because I have read that the ground truth is the same as a label in ...
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Why don't ensembling, bagging and boosting help to improve accuracy of Naive bayes classifier?

You might think to apply some classifier combination techniques like ensembling, bagging and boosting but these methods would not help. Actually, “ensembling, boosting, bagging” won’t help since their ...
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Can I train a neutral network to detect a fingerprint [closed]

I have been using tensorflow For neutral network
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How do weak learners become strong in boosting?

Boosting refers to a family of algorithms which converts weak learners to strong learners. How does it happen?
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What is the difference between parametric and non-parametric models?

A model can be classified as parametric or non-parametric. How are models classified as parametric and non-parametric models? What is the difference between the two approaches?
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1answer
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Is there a connection between the bias term in a linear regression model and the bias that can lead to under-fitting?

Here is a linear regression model $$y = mx + b,$$ where $b$ is known as $y$-intercept, but also known as the bias [1], $m$ is the slope, and $x$ is the feature vector. As I understood, in machine ...
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1answer
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Comparing a large/general CNN to a smaller more specialized one?

I am still somewhat a novice in the ML world, but I had a strange idea about CNNs and wanted to ask if this would be a valid way to check the robustness of a general CNN that classifies certain images....
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How does Hartigan & Wong algorithm compare to Lloyd's and Macqueen's algorithm in K-means clustering?

As far I know, this is how the latter two algorithms work... Lloyd's algorithm Choose the number of clusters. Choose a distance metric (typically squared euclidean). Randomly assign each observation ...
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What are examples of under-parametrization and over-parametrization in machine learning? [duplicate]

Today, I heard from a colleague that traditional ML works with under-parametrization while deep learning works with over-parametrization. Are there examples to illustrate the meaning of these two ...
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Ignoring model testing at neural networks

I've already collected a small dataset to estimate neural networks model for prediction purposes. My question is skipping the testing stage at neural networks such as General Regression Neural Network ...
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Neural network algorithm implementation for Iris dataset

I want to use Neural network algorithm over famous Iris dataset. Iris dataset attributes names sepal length in cm sepal width in cm petal length in cm petal width in cm Sample dataset: ...
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Can someone explain and help to understand this fuzzy diagram?

Could someone help me to understand in detail each step of this fuzzy diagram, because I am lost?
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Why is the learning rate generally beneath 1?

In all examples I've ever seen the learning rate of an optimisation method is always < 1. However, I've never found an explanation as to why this is. In addition to that, there are some cases where ...
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1answer
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Is this ML task possible?

What I want to do is from an Internet challenge to transform any given image into the Polish flag using the available filters and crop tool on the iPhone camera app. Here's an example. There aren't ...
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1answer
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What's the threshold to call something 'machine learning'?

For example, if I use some iterative solvers to find a solution to a non-linear least squares problem, is that already considered machine learning?
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How do you make a regression model from a binary labeled dataset?

Suppose I have a dataset with hand images. Hand completely opened is labeled as 0 and hand completely closed (fist) are labeled as 1. I also have a bunch of unlabeled images of hands which, if ...
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What would be a typical pre-processing and data normalization pipeline for time series data (for non-linear models such as neural networks)?

I've started to work on time series. I was wondering what would be the best data normalizing and pre-processing technique for non-linear models, specifically, neural networks. One I can think of is ...
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1answer
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How much overfitting is acceptable?

I have a deep learning configuration in which I obtain good results on the validation set but even better results in the training set. From my understanding this means that there is overfitting to ...
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What is the goal of weight initialization in neural networks?

This is a simple question. I know the weights in a neural network can be initialized in many different ways like: random uniform distribution, normal distribution, and Xavier initialization. But what ...
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Estimating an $n$-Gram model using on bigrams

One of the main arguments against $n$-gram models is that, as $n$ increases, there is no way to compute $P(w_n|w_1,\cdots,w_{n-1})$ from training data (since the change of visiting $w_n,...,w_1$ is ...
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What are some good models to use for spelling corrections?

I used OCR to extract text from an image, but there are some spelling mistakes in it : The text is as follows : ...
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Is there a UCB type algorithm for linear stochastic bandit with lasso regression?

Why is there no upper confidence bound algorithm for linear stochastic bandits that uses lasso regression in the case that the regression parameters are sparse in the features? In particular, I don't ...
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What are examples of AI theories that are structurally complex but ontologically simple?

From the Wikipedia article on Occam's razor: Another contentious aspect of the razor is that a theory can become more complex in terms of its structure (or syntax), while its ontology (or semantics) ...
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Multiple Inertia sensors system based for gestures recognition

I am a newbie to Machine Learning field as I am engaging to a personal project that I am trying to use the 6 degree of freedom Inertial Measurement Units(IMUs) measuring the Acceleration acting on 3 ...
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How to build a database of image data for machine learning? [migrated]

I want to build a database of image data for machine learning. But how should this be done? I'm assuming people don't just dump all of their image data into a folder? Do they use a relational database ...

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