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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The extend of machine learning in mathematical analysis

As a student of Numerical analysis, I can see how mathematical analysis involved in making a language program specifically in the convergence analysis of an approximation method. But, while chatting ...
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What is the 'label' column here in California housing example of Machine Learning?

https://developers.google.com/machine-learning/crash-course/california-housing-data-description I have also attached a snap but I am confused which column(s) is label? and which columns are features? ...
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can you get the exacte mathematical expression of a ANN model

I know that you can estimate the ANN model, but is it possible to get the exact mathematical expression, is there any work that proves that it's possible/not.
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What is a validation data set in Machine learning? [duplicate]

https://www.youtube.com/watch?v=i_LwzRVP7bg&list=PPSV&ab_channel=freeCodeCamp.org I was watching above youtube video , in chapter "Training Model" there were 3 sets discussed. 1)...
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What is the difference between the term "generative" in classical machine learning and deep learning?

There are lots of explanations on DGM (Deep Generative Model) and generative classifier (most of the explanations on which are about generative classifier vs discriminative classifier) But, I can ...
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Would it be a problem to use non-squared images for a CNN model?

I want to create a music sheet scanner using CNN Model and the images I am using are not squared and, if I make them squared, important data will be lost and it might confuse the model. Is it ok to ...
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Please help me understand the role of loss function in neural networks

I've been studying NNs with tensorflow and decided to code a simple NN from scratch to get a better idea on hwo they work. It my understanding that the cost is used in backpropagation, so basically ...
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New to AI tools, looking for image interaction AI [closed]

I'm new to AI and looking for an AI that can interact with a graphical user interface (GUI) Something that can recognize shapes, forms and colors and can interact with them just as we use keyboard and ...
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1 answer
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Causal Inference: Understanding the impact of an intervention

I'm trying to create a pipeline for a very common business scenerio. I want to see whats the impact of an intervention on an outcome. For example I want to know if I send a marketing email (...
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3060 12gb for machine learning [closed]

Has anyone used 3060 12gb for machine learning.I wanna learn machine learning but had the budget for Rx 6700xt as 3060 ti is quite expensive but 3060 12 gb support cuda so do i need a GPU or can I use ...
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Where can I find research on Active Learning and life-long AI research. Who are the leaders in these respective fields? [closed]

I would love to explore some of the cutting edge research in AI systems and Machine Learning. I hear life-long learning is going to be crucial to the field as well as the Alignment problem. If anyone ...
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Since ReLU activations also result in a sparse network, does it have the same "feature selection" property as L1 regularization?

From Deep Learning (Courville, Goodfellow, Bengio), a ReLU activation often "dies" because One drawback to rectified linear units is that they cannot learn via gradient based methods on ...
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When training a DNN on infinite samples, do ADAM or other popular optimization algorithms still work as intended?

When training a DNN on infinite samples, do ADAM or other popular optimization algorithms still work as intended? I have an DNN training from an infinite stream of samples, that most likely won't ...
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How does Deep Q network converge [duplicate]

EDIT: The linked questions do not answer my question. My question is, how is reward incorporated into the following algorithm to make it improve iteratively When the target reward is random. Following ...
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What is wrong with my PyTorch model training on CIFAR10?

I am training a ResNet model on CIFAR10 dataset. For the training subset, I selected a random 1% of the train data from the default train/test split. For the test subset I used the whole default test ...
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How to output a function given a time series data as an input using supervised learning?

I have a spreadsheet with time series data collected from two sensors, one measuring temperature and the other measuring humidity. And I also collected data from an experiment that I conducted, the ...
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What is a good strategy for breaking up content into prompts and completions for OpenAI fine tuning?

I want to train a fine-tuned openai model to know more about specific Judo throws and training methodologies. I have a bunch of documents I have written on Judo throws that I would like to use for ...
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InstructGPT: What is the sigma in the loss function and why $\log(\cdot)$ is being used?

InstructGPT: What is the sigma in the loss function and why $\log(\cdot)$ is being used? $$ \operatorname{loss}(\theta) = -\frac{1}{\binom{K}{2}}E_{(x,y_w,y_l)\sim D}[\log(\sigma(r_{\theta}(x, y_w) - ...
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Reverse Distribution in Denoising Diffusion Models is Simple

In explanations of denoising diffusion models it is stated that $q(x_{t-1}|x_t)$ is intractable. This is often justified via Bayes' rule, i.e. $$ q(x_{t-1}|x_t) \propto q(x_t|x_{t-1})q(x_{t-1}) $$ and ...
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How "exactly" are AI-accelerator chip ASICs built differently than GPUs as GPU seem to lead for many AI workloads on performance

There is a lot of discussion on google search about AI-custom-accelerators (like Intel's Gaudi) and GPUs. Almost all of them say generic things like, a) AI Accelerator chip is for specialized AI ...
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Does chatGPT learn or remember from (public) user input? Will it 'fess up to it? I could not get it to reveal [closed]

It started with a question inspired by this video: New Research Suggests to Put AI to Sleep https://youtu.be/0yuQlbCkTJ0 She says: "In this video I discuss a new research paper which suggest a ...
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How do we determine what is correct and what not in Adaboost

In Adaboost, how is it determined what is correct and what not? In the following example from StatQuest (in youtube), what correct is and what incorrect makes sense in real life. But what if we have a ...
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Does this look like overfitting?

I'm using a Decision Tree that gave me great test metrics. Then I checked the learning curve, but it seems a little strange to me regarding the training score. Do you think there is a problem with ...
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1 answer
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Should a CNN generalize to arbitrary positions in the data?

I have trained a CNN on one dimensional data that is the power spectral density (PSD) of a $N$ different classes of signals ($N=4$). Each of the $N$ signals has a different spectral shape (not shown ...
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Is it possible to train an AI to organize frames in chronological order?

I would like to know if there is a neural network or some other kind of AI that would be able to reconstitute randomly shuffled frames into a video or slideshow that makes chronological sense? The ...
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Training to recognize tempo from conductor hand motion

What existing algorithm would be the best for training a model to recognize the timestamps when musical beats are occurring based on a discrete time-domain conductor hand motion track? Input: time ...
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Which calculation to use for GRU

Im doing trying to implement GRU in my own Neural Network Library but when I did some research i stumbled on some inconsistencies. When calculating a cell there are as many legitimate resources which ...
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ML Network Traffic Classification Problem

I am hoping for suggestions or advice as to whether ML offers a suitable solution to the below problem. I am not so familiar with ML techniques so apologies if this is a straight forward question. I ...
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Does lazy learning require train-test-validation split?

This is a follow-up question to another post on SE AI that asked to distinguish lazy and eager learning. One answer said that lazy learners do not require training and do all of the computation ...
2 votes
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Why does ChatGPT creates fake code?

ChatGPT has been a big thing lately. It also makes a lot of mistakes. For example, it creates fake functions of a package and tells it as it works for real. I was wondering how that works. Why is it ...
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Is VAE the same as the E-step of the EM algorithm?

EM(Expectation Maximum) Target: maximize $p_\theta(x)$ $ p_\theta(x)=\frac{p_\theta(x, z)}{p_\theta(z \mid x)} \\\\$ Take log on both sides: $ \log p_\theta(x)=\log p_\theta(x, z)-\log p_\theta(z \...
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How many opposing users should be recorded exterior to the average data before being combined?

Outside of the programming toward AI, I am having difficulty putting together a plan on how this machine I hope to build would work. The basic question is: How should it handle user reviews / ...
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What are the inputs of a neural network when learning a difference equation?

The time series y[n] is the solution of the difference equation ...
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How to optimize ELBO(VAE's loss function)?

Suppose we've got the following formula: $\log p(x;\theta)=\mathbb{E}_{q(z|x;\phi)}[\log p(x,z;\theta)-\log q(z|x;\phi)]+KL(q(z|x;\phi)||p(z|x;\theta))\\ \geq \mathbb{E}_{q(z|x;\phi)}[\log p(x,z;\...
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Machine Learning for raw measurement data

i have raw measurement data of different events. My first approach was to calculate features of those events, do scaling, PCA and feature selection and then feed those features to different machine ...
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Doesn't every single machine learning classifier use conditional probability/Bayes in its underlying assumptions?

I'm reading about how Conditional Probability/ Bayes Theorem is used in Naive Bayes in Intro to Statistical Learning, but it seems like it isn't that "groundbreaking" as it is described? If ...
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Which correlated feature should be eliminated from a model?

BACKGROUND: There is a lot of information online about the problem of multicollinearity as it relates to machine learning and how to identify correlated features. However, I am still unclear on which ...
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Can AI/ML be used to decipher unknown text on such a low resolution scale of one or two letters to a pixel by color?

Can an image of unknown text, with resolution of about one or two letters in real life represented by a pixel (like if the image is a photo taken from far away), be used by machine learning or AI to ...
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What does this bracket notation $\langle\phi(x),v\rangle$ mean?

I found it at the bottom of page 2 of the paper Intriguing properties of neural networks (2014), in the form of $$\underset{x\in\mathcal{I}}{\mathrm{arg\,max}}\langle\phi(x),v\rangle$$
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Ways to Reduce False Positive or False Negatives in Binary Classification (0,1)

I am working on a task in which I need to classify binary labels 0 and 1 properly (as close to perfection as possible). My final dataset (ready for classification) has input data with 141 features and ...
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How can I formulate a usecase with an additional constraint as a reinforment learning problem?

I am new to the field of reinforcement learning, and I feel a recent use case of mine is highly relevant, but I don't know how to forumate it as a typical reinforcement learning problem. Let's say I ...
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Do adversarial samples violate the i.i.d. assumption?

I am trying to understand why adversarial attacks work in theory. I have read, that the image is perturbed by a special perturbation $X_{adv}=X_1+p$, but i can't find any reference on that ...
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How does dropout work during backpropagation?

I've searched for an answer to this, and read several scientific articles on the subject, but I can't find a practical explanation of how Dropout actually drops nodes in an algorithm. I've read that ...
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How can I learn about NN architecture?

I have a pretty good understanding of individual neural net layers (fully connected, convolution, pooling, activation, etc) but struggle to construct combinations of them to solve a given problem. I ...
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How does ChatGPT respond to novel prompts and commands?

So I understand how a language model could scan a large data set like the internet and produce text that mimicked the statistical properties of the input data, eg completing a sentence like "eggs ...
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Is there any different evaluation metrics(Performance Metrics) for Deep learning ,Machine, learning and NLP?

I'm a little confused about machine learning. I know we can use accuracy, and precision-recall when it comes to a classification problem, and when it comes to regression problems, we usually go with ...
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What is the most abstract mathematical treatment of machine learning?

The essential characteristic of machine learning is that an algorithm can discover the behavior of a system on its own. Neural networks are a foremost example of this. But what property do neural ...
1 vote
1 answer
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Are there any books that teach text-to-image generation?

I read some of the research papers about text-to-image generation using Imagen, DALL-E 2, etc. but they are heavily scientific and I don't understand a lot of their concepts, so I was wondering are ...
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How the Critic is used to train the Actor in Actor-Critic network

I understand the general idea behind the Actor-Critic architecture. The actor maps state to action, and the critic maps state + action to reward. But I don't fully understand how the critic output (...
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Modeling the previous inputs to affect next output in Machine learning

I am working on a dataset contains one output variable and a number of input variables.The data looks like the following: Y, X1, X2, X3, X4 7, 5, 0.7, 8, 9 3, 6, 0.3, 9, 9 .... Where Y is the output ...

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