6 votes

Is it necessary to know the details behind the AI algorithms and models?

This is a good question. I tend to think the answer is yes it is necessary to know the details, because a person without mathematical understanding of these algorithms cannot consistently make a model ...
5 votes
Accepted

What are the most challenging tasks aiming to achieve the lowest error rate?

Yes. Here are some of the most prominent ones and their respective state-of-the-art errors: CIFAR-10: ~3.5% error CIFAR-100: ~24% error STL-10: ~26% error SVHN: ~1.7% error ImageNet tasks: the best ...
  • 2,039
4 votes
Accepted

Musical notes interpretation

AI/ML can solve the task described, a solution is as below: Regular image processing algorithm (pixel row with min black pixels, adjacent rows are considered as 1) to split the sheet music (as image) ...
  • 1,223
4 votes

How to implement exploration function and learning rate in Q Learning

Your main problem is that you need to separate out what is driving the behaviour policy from the Q-table. Q Learning is an off-policy algorithm. The Q-table that it eventually learns is for an optimal ...
  • 24.6k
4 votes

What should we do when we have equal observations with different labels?

The problem you are portraying looks like a modified XOR problem. You can't throw away the lines with a label of 1 because a the model won't be able to learn this class.
  • 141
4 votes

What is the relationship between data science, artificial intelligence,machine learning and computer vision?

This Venn diagram might help to visualize the relation between the different fields: The image is from the free deep learning book by Ian Goodfellow, Yoshua Bengio and Aaron Courville. As you said ...
2 votes
Accepted

How to rescale data to its original range after MinMaxScaler?

You can use the function inverse_transform of the created MinMaxScaler object. See also this Stack Overflow question for other ...
  • 35k
2 votes
Accepted

How do I know if my dataset is ready for a machine learning model?

Before jumping to modeling, there are a few tasks a data scientist (or ML/AI practitioner) must do: Ideation (or hypothesizing): Before applying any modeling approach, we need to ask the right ...
2 votes
Accepted

What data formats/pipelining are best to store and wrangle data which contains both text and float vectors?

There are different possible ways to handle huge datasets: If the data is too big to be fully uploaded to RAM, you can iterate over it in Pandas. You can find a brief explanation in the article ...
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2 votes
Accepted

How to define the "Pre-Processing" in machine learning?

Data preprocessing consists of all those techniques used to generate the final datasets (with an appropriate size, structure, and format) for the machine learning algorithms or models. Data ...
  • 35k
2 votes

Is this dataset with only two features suitable for clustering with k-means?

One problem with clustering algorithms is that they will typically find you a solution, ie they will split your data set into clusters, but it will find you a structure even if there isn't one. Your ...
  • 5,167
2 votes
Accepted

How can I address missing values for LSTM?

You can claim to use a real-world dataset, you would just need to specify that some values were interpolated. Do you have to have the inter-mediate values though? By the looks of it, each "region&...
  • 1,266
2 votes
Accepted

How to tackle the human error made in labeling datasets for classification tasks like facial expression recognition?

In general the only way to deal with this is by quantifying these labeling mistakes in the output of the model, since the model will learn for them. And in many cases these are not really mistakes, ...
2 votes

Musical notes interpretation

LMGTFY ;) The problem is called Optical Music Recognition. Here you can find tutorial that desribes OMR with deep learning and here you have scientific paper. I think it is very good start for your ...
1 vote

What are the possible ways to handle imbalance in multi-class image datasets?

Yes this is certainly possible... What you want to do is apply a weight to particular classes by proportion of the imbalance(assuming nothing else related to the problem is of note). See this post for ...
1 vote
Accepted

Generating a dataset from data with "assumed" lables

I think you need to look into semi-supervised learning, which combines supervised and unsupervised learning for problems where large labelled datasets are not available. To use this family of ...
  • 406
1 vote

Is there a clustering algorithm that can make n clusters and the n+1 "others" cluster?

So, I've prepared some data that resembles your sketch: ...
  • 1,833
1 vote

Predicting a day's data

Just for clarification: your description (1 sample per minute) does not match the example data (far fewer data points which is understandable, but also two data points in one minute which contradicts ...
1 vote
Accepted

Can alpha-beta pruning be used for applications apart from games?

Thinking about this more, the answer is in fact yes, but not for the application you mention. You cannot use alpha-beta pruning to learn a model to predict customer outcomes, because it is only ...
1 vote

How to calculate the false positives and negatives?

Yes, you can use sklearn's confusion_matrix. To explicitly extract the false positives and negatives, you can do ...
  • 35k
1 vote

What should we do when we have equal observations with different labels?

This is perfectly acceptable in a stochastic environment. Generally your loss is to minimize $-log\ p(Y|X)$ or equivalently $-\sum_i log\ p(y_i|x_i)$. This optimization is equivalent to $-\mathbb{E}\...
  • 2,299
1 vote

Automatic prediction of whether a customer will come into the shop or not

This should be possible, but it's not completely clear what you are trying to do. If you're trying to predict customer age and gender from a video, then you've got a computer vision problem. Deep ...
1 vote

What types of machine learning model would fit?

Welcome to AI.SE @Par! What you have might be either a multi-label or a multi-class classification problem. If the classes are disjoint (each example belongs to just 1 of the 50 classes), it's a ...
1 vote

Loss/accuracy on Synthetic data

No, there is no difference. Of course, you are likely not able to extrapolate results obtained from synthetic data to expect identical or similar results in real life to unless you have very ...
1 vote
Accepted

Parameters to calculate affluence in localities of Metro city

Affluence could encompass several parameters: Income; Wealth (property ownership); Life expectancy; Access to services such as education and health; Access to clean natural resources; Low levels of ...
1 vote
Accepted

Which field to study to learn & create a.i generated simulations?

You need to define "simulation" more specific. Playing Mario, Swapping face on image/video, or generating simulation of objects that are orbiting use different techniques. Playing Mario or "AI that ...
  • 2,571
1 vote

What is the impact of scaling the features on the performance of the model?

In general, algorithms that exploit distances or similarities (e.g. in the form of scalar product) between data samples, such as k-NN and SVM, are sensitive to feature transformations. We do feature ...
1 vote

is it possible to train several Neural Networks on different types of data and combine them?

The term you need is “model ensembles”, that’s the way models are combined. Pretty hard to be more specific since you don’t give a language or any other details.
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1 vote

How can I evaluate the performance of a system that generates text?

Human evaluation is the gold standard as stated in this podcast by Asli Celikyilmaz, even if you only test a very small part of the generated text. You needed an automated method and this one: BLEURT ...

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