14 votes
Accepted

What are the different approaches used in Machine Learning?

Things in italics should give you enough googleable terms to start a deeper dive :P. There are 3 main branches of statistical ML. Supervised Learning This approach is taken when a problem can be ...
Jaden Travnik's user avatar
11 votes

What is the difference between self-supervised and unsupervised learning?

In self-supervised learning (SSL) you use your own inputs $x$ (or a modification, e.g. a crop or with data augmentation applied) as the supervision. Instead, in unsupervised learning (UL) there is no ...
Luca Anzalone's user avatar
9 votes
Accepted

How does an unsupervised learning model learn?

Supervised learning is typically an attempt to learn a mathematical function, $f(\bf X)=\bf y$. For this, you need both the input vector $\bf X$ and the output vector $\bf y$. The model outputs have ...
Matthew Gray's user avatar
  • 4,262
6 votes
Accepted

What is the calcium equivalent role in neural networks

Neural networks don't model biological neurons. They are at best inspired by biological neurons, in that they get excited by certain inputs and fire once the excitation crosses a threshold. And this ...
BlindKungFuMaster's user avatar
5 votes

What is graph clustering?

In graph clustering, we want to cluster the nodes of a given graph, such that nodes in the same cluster are highly connected (by edges) and nodes in different clusters are poorly or not connected at ...
nbro's user avatar
  • 40.5k
5 votes
Accepted

What is the “Hello World” problem of Unsupervised Learning?

I disagree with the context that MNIST is the "hello world" of supervised learning. It is definitely, though, the "hello world" of image classification, which is a very specific ...
Djib2011's user avatar
  • 3,183
5 votes

What is the difference between self-supervised and unsupervised learning?

TL;DR While both methods learn from data without human-annotated labels, the primary difference lies in the way they use the data: Self-supervised learning makes use of the structure within the data ...
ofou's user avatar
  • 151
4 votes
Accepted

Which unsupervised learning technique can be used for anomaly detection in a time series?

So if I understood correctly: You have data from 2 sensors in time: Ar flow and BackGas Flow (SCCM, what is that?) You have that data for multiple products. 1 - Since it is relatively low dimensional, ...
Pedro Henrique Monforte's user avatar
3 votes
Accepted

Is there a way to perform pattern recognition without a labeled training set?

You should look into unsupervised learning, which is machine learning without a human-labeled training set.
k.c. sayz 'k.c sayz''s user avatar
3 votes
Accepted

Which Reinforcement Learning algorithms are efficient for episodic problems?

When applying techniques like SARSA(which are on-policy), one needs to have control over a simulator. If one is able to access only the episodic dataset, then the only choice is to opt for Q-learning ...
Rahul's user avatar
  • 46
3 votes
Accepted

Where can I find an implementation of the wake-sleep algorithm?

I found the following detailed and well documented Python notebook, which uses only NumPy.
TheCG's user avatar
  • 156
3 votes
Accepted

Which unsupervised learning algorithm can be used for peaks detection?

If your anomalies are simply peaks, why should you be using machine learning methods? You could use peak detection algorithms for the purpose. If you still insist on ML, isolation forest is a good ...
Arun Aniyan's user avatar
3 votes
Accepted

Is there a machine learning algorithm to find similar sales patterns?

If I understand correctly you want to find companies with similar patterns to yours. I would start with measuring cosine similarity between your company and ...
Akavall's user avatar
  • 216
3 votes
Accepted

Is unsupervised learning a branch of AI?

There is a problem with confining Artificial Intelligence to a single definition, because it has become an umbrella term encompassing many fields of science. It has come a long way from the "thinking ...
Djib2011's user avatar
  • 3,183
3 votes
Accepted

How can reinforcement learning be unsupervised learning if it uses deep learning?

Supervised learning The supervised learning (SL) problem is formulated as follows. You are given a dataset $\mathcal{D} = \{(x_i, y_i)_{i=1}^N$, which is assumed to be drawn i.i.d. from an unknown ...
nbro's user avatar
  • 40.5k
3 votes
Accepted

Reinforcement Learning vs Supervised Learning

Your statements are mostly incorrect, there are very large differences between reinforcement learning (RL) and supervised learning (SL). In SL, you have labels that should be the correct answer that a ...
Dr. Snoopy's user avatar
  • 1,355
2 votes
Accepted

Why does unsupervised pre-training help in deep learning?

Unsupervised pre-training was done only very shortly, as far as I know, at the time when deep learning started to actually work. It extracts certain regularities in the data, which a later supervised ...
BlindKungFuMaster's user avatar
2 votes

Which machine learning algorithm can be used to identify patterns in a dataset of the cache performance of a CPU?

You're basically looking for is unsupervised learning (UL). There are a lot of UL techniques around, but I'm not sure you'll find one that does exactly what you want with no user input at all. Still, ...
mindcrime's user avatar
  • 3,757
2 votes
Accepted

Does neuroevolution require a labelled dataset?

The GA will require a fitness function, which means you need labeled data for comparison. That conclusion is wrong. Yes, sometimes your fitness function will use labeled data. For example, if you ...
Thomas Wagenaar's user avatar
2 votes
Accepted

How can a neural network work with continuous time?

By the way you have explained things above, it seems more like a problem with your code and not the something to do with the environment. The term discrete and continuous is used to define, how the ...
Ugnes's user avatar
  • 2,023
2 votes
Accepted

Do Le et al. (2012) train all three autoencoder layers at a time, or just one?

The paper refers to layers and sub-layers, and clearly indicates that one layer includes all three sub-layers, so when they say they train all three layers simultaneously, they are talking about the ...
Josiah Yoder's user avatar
2 votes
Accepted

Has anybody tried unsupervised deep learning from youtube videos?

Answer is quite yes, please have a look what Google did around this: Google Cloud Video Intelligence makes videos searchable, and discoverable, by extracting metadata with an easy to use REST API. ...
mico's user avatar
  • 937
2 votes

Has anybody tried unsupervised deep learning from youtube videos?

Yes! Unsupervised machine learning has absolutely been applied to youtube videos... To recognize cats! Here's an article about it in wired. One of the leading ML researchers was Andrew Ng.
Charles's user avatar
  • 291
2 votes

Has anybody tried unsupervised deep learning from youtube videos?

Yes, it is possible, and yes, it probably has been done before. Odds are, however, the person(s) who tried were disappointed with the results and forgot to tell others. The reasons they might be ...
FreezePhoenix's user avatar
2 votes

Will artificial super-intelligence evolve to have selfishness inherent in biological systems?

AI will only "evolve" selfishness if it "evolves" in a competitive environment and has certain human-like faculties. Self-protecting desires on the other hand are logical consequences of having any ...
BlindKungFuMaster's user avatar
2 votes

Why do Decision Tree Learning Algorithm preferably outputs the smallest Decision Tree?

Adding to SmallChess's answer , Larger trees(with many nodes) are too adapted to the training set, as a small change in the input train data might cause the trees to change very much and hence change ...
Fenil's user avatar
  • 181
2 votes

Why do Decision Tree Learning Algorithm preferably outputs the smallest Decision Tree?

The bigger your tree is the more overfitting your model is. In machine learning, we always prefer a simpler model unless there is good reason to go for complication.
SmallChess's user avatar
  • 1,411
2 votes
Accepted

Given a set of images that are not divided into groups, which algorithm should I use to do that?

This is called "clustering" , If the network is already trained with data that has similar features as of the "symbols", you can use that network with its last classification layer removed , then run ...
thecomplexitytheorist's user avatar
2 votes
Accepted

What is the relationship between these two taxonomies for machine learning with neural networks?

Could you please let me know which of the following classification of Neural Network's learning algorithm is correct? The first one classifies it into: supervised, unsupervised and ...
Dennis Soemers's user avatar
  • 10.3k
2 votes

Why isn't the Credit Card Fraud Detection dataset from Kaggle already balanced?

I believe that the idea is to have a similar ratio of fraud/"normal transaction" as to the ones that bank encounter on real life. If you balance it you will probably have a lot of false positive once ...
kirua's user avatar
  • 424

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