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Results tagged with machine-learning
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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.
1
vote
Why are PyTorch and TensorFlow the most widely used frameworks?
I didn't check and only an extensive survey can provide good evidence for your claim, but, yes, it's most likely true that PyTorch and TensorFlow are among the most widely used libraries. There can be …
- 41.4k
1
vote
Can software testers transfer their skills into adversarial testing for AI/LLMS?
One easy way to test language models is to provide prompts and check if they return an acceptable answer (which you should define) or they don't return a bad answer, for example, a factually wrong ans …
- 41.4k
2
votes
Why does model overfitting lead to poor generalization?
Note that if you know the function your data was generated from, you do not even need machine learning. For example, if you generated the data with $y = f(x) = x^2$, you already have the function to p …
- 41.4k
4
votes
What are the differences between seq2seq and encoder-decoder architectures?
They are not the same, but they can overlap.
An encoder-decoder architecture is composed of an encoder (which compresses the input) and a decoder (which decompresses the compressed input).
A sequence- …
- 41.4k
4
votes
What is a pipeline in machine learning?
A data pipeline consists of 3 main steps
data collection (e.g. you collect images of cats from different sources)
data transformation (e.g. you make the images all have the same dimensions and maybe …
- 41.4k
7
votes
Accepted
What's the difference between estimation and approximation error?
Section 5.2 Error Decomposition of the book Understanding Machine Learning: From Theory to Algorithms (2014) gives a description of the approximation error and estimation error in the context of empir …
- 167
1
vote
Accepted
What is the difference between Machine Learning model, algorithm and hypothesis?
An algorithm is a sequence of instructions to tell the computer (or a human) what to do. The computer executes the algorithm and produces or not (may not halt) an output (e.g. prints a message). A Pyt …
- 41.4k
4
votes
What exactly is meant by variational distribution?
The variational distribution is the distribution (or set of distributions) that you use to approximate the distribution you are looking for. It's often denoted by $q$, $q_\phi$ or $q_\phi(z \mid x)$, …
- 41.4k
3
votes
How to expand reconstruction error to mean squared error in Variational AutoEncoder?
In a way, you're right. The reconstruction loss is just an idea because you have not yet defined the distribution $p_\theta$. If you assume that this distribution is e.g. a Gaussian, then you should b …
- 41.4k
5
votes
Accepted
What is the difference between features and inputs in machine learning?
An input usually refers to an example (sometimes also known as sample, observation or data point) $x$ from a dataset that you pass to the model. For example, in supervised learning, you have a labelle …
- 103
4
votes
What makes ChatGPT a generative model?
What are generative (and discriminative) models?
If the model learns a distribution of the form $p(x)$ or $p(x, y)$, where $x$ are the inputs and $y$ the outputs/labels, from which you can sample data …
- 41.4k
4
votes
Whys and Why-nots using Rust for AI
I think you mentioned the most important points.
I love Rust, but I believe that most ML/AI practitioners and researchers would find it harder to use and it would slow them down - many AI/ML practitio …
- 41.4k
1
vote
Autoencoders: Where does the encoder end and the decoder begin?
I think that, in the case of a (deterministic) auto-encoder that only compresses the data, which is trained with MSE, it's an assumption or convention that the encoder is just that part of the neural …
- 41.4k
3
votes
Accepted
What other Machine Learning techniques other than Neural Networks are there?
There are many techniques (algorithms and models) in ML other than neural networks, for example
decision trees
support vector machines
hidden Markov models
Bayesian networks
linear regression
k-means …
- 41.4k
2
votes
Accepted
How does Bishop derive $\ln p\left(\mathbf{x} \mid \mu, \sigma^{2}\right)$, when $p$ is a Ga...
This is not so difficult (just a bit verbose if you do all steps). Just replace $\mathcal{N}\left(x_{n} \mid \mu, \sigma^{2}\right)$ with $\frac{1}{\left(2 \pi \sigma^{2}\right)^{1 / 2}} \exp \left\{- …
- 41.4k