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17 votes

How can I encode angle data to train neural networks?

The main problem with simply using the values $\alpha \in [0, 2\pi]$ is that semantically $0 = 2\pi$, but numerically $0$ and $2\pi$ are maximally far apart. A common way to encode this is by a vector ...
Chillston's user avatar
  • 1,748
8 votes
Accepted

How are sentences numerically encoded before passing them to neural networks?

On a very basic level, you are absolutely correct about the encoding of the attached sentence. But, practically, when you have a set n number of documents to be encoded, things happen differently. ...
Chinmay's user avatar
  • 531
5 votes
Accepted

What is "conditioning" on a feature?

This is conditioning in the sense of conditional probability. The idea is that the authors have some "standard physically-inspired features". They are splitting the data up into bins based on the ...
John Doucette's user avatar
4 votes
Accepted

Should I grey-scale the coloured frames/channels to build the approximation of the state?

We can't say for sure which approach would work best in the general case. If you have domain knowledge, you can make a better guess. You'll basically want to answer the question: which information is ...
Dennis Soemers's user avatar
  • 10.4k
4 votes

How should I deal with variable-length inputs for neural networks?

The most common way people deal with inputs of varying length is padding. You first define the desired sequence length, i.e. the input length you want your model to have. Then any sequences with a ...
Djib2011's user avatar
  • 3,193
4 votes
Accepted

How to solve the problem of too big activations when using genetic algorithms to train neural networks?

Your inputs should stay in a low range. Ideally for neural networks, the inputs are normalised to mean 0, standard deviation 1. I suspect this applies equally well to GA-driven NNs as gradient-driven ...
Neil Slater's user avatar
  • 32.7k
4 votes
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Does the term "data augmentation" imply increasing the training dataset?

I'm not familiar with any "authoritative" single definition somewhere, or not sure who used the term first, but I would personally indeed agree with the reviewer you mention. In fact I've ...
Dennis Soemers's user avatar
  • 10.4k
3 votes

Why do we normalize data in a deep neural network?

I answered a similar question earlier and here is a piece of my answer that i think covers your question: Batch normalization's assistance to neural networks wasn't really understood for the longest ...
mshlis's user avatar
  • 2,379
3 votes

Would this relatively small dataset be enough to train a CNN?

Your data set would be what is called "unbalanced' and this can lead to problems in developing an accurate classifier. The best thing to do (which you might not be able to do) is to find more ...
Gerry P's user avatar
  • 724
3 votes

Would this relatively small dataset be enough to train a CNN?

It is somewhat risky to discuss data independently with your learning mechanism. There is actually no such thing as good data or a good learner. There is only data that is good WITH a particular ...
Douglas Daseeco's user avatar
3 votes
Accepted

Does data skew matter in classification problem?

Yes. Skewed data is one of the biggest problems in AI applications. As you rightly identified, the real world distribution is skewed. Doing a random sampling results in one major issue of an uneven ...
Naveen Venkat's user avatar
3 votes
Accepted

How does the RL agent understand motion if it gets only one image as input?

In the article Playing Atari with Deep Reinforcement Learning, Mnih et al, 2013, which was a major outbreak in Deep Reinforcement learning (especially in Deep Q learning), they don't feed only the ...
16Aghnar's user avatar
  • 601
3 votes

How to perform prediction when some features have missing values?

You should look into "missing values". This is an entire research field in itself. First, you need to identify the type of missing values: They can be missing purely at random. Whether they ...
Robby Goetschalckx's user avatar
3 votes

Why do we resize images before using them for object detection?

There are different questions and even different lines of thought here. Let's go through them On resizing Why do we need to resize? To fit the network input which is fixed when nets are no Fully ...
JVGD's user avatar
  • 1,148
3 votes
Accepted

In OCR, how should I deal with the warped text on the sides of oval objects?

There are many papers on this but the following is a good start: How to unwrap wine labels programmatically. The author includes source code in Python. You mentioned you do not want to do a ...
Brian O'Donnell's user avatar
3 votes
Accepted

Rescaling time-series data with very spiky pattern for training data in LSTM network

First, if your data has a minimum of 0 and maximum of 5000, 1000 will get rescaled to .2 and 5000 will get rescaled to 1. So it's not a .001 difference as you suggest. If you just used a regular loss ...
Taw's user avatar
  • 1,291
3 votes

Process 2TB worth of conversational data hoarded over 40 years. How can I pass this into GPT to ask questions about it?

Two approaches that I am aware of: Chat your data This GitHub repository is accompanied by a blog post on how it works schematically. The overall approach is based on the LangChain library. Azure ...
Hans-Peter Schrei's user avatar
3 votes
Accepted

Which preprocessing is the correct way to forecast time-series data using LSTM?

A standard method for pre-processing time series data for neural network architectures, such as an LSTM, is to normalize the data. Good tutorials will include this step. There are several variations ...
Brian O'Donnell's user avatar
3 votes
Accepted

Is it a requirement/recommendation to normalize my inputs into [0,1] range?

Generally, between -1 and 1 are ideal, though you can get away with a wider range. For example, using the z-score as the range, you will be outside of this range, sometimes by quite a bit (say, -30 ...
David Hoelzer's user avatar
2 votes
Accepted

Why is the short-time Fourier transform used for preprocessing audio samples?

Fourier transform is used to transform audio data to get more information (features). For example, raw audio data usually represented by a one-dimensional array, ...
malioboro's user avatar
  • 2,829
2 votes
Accepted

How should I encode the input which are 5 cards from a deck of 52 cards?

Depends on how your game is played. Is there any meaning assigned to the order of cards, or are all 5 played simultaneously? If order matters, use 5 one-hot vectors so you can choose how to order them,...
cantordust's user avatar
2 votes

Do I need to use a pre-processed dataset to classify comments?

Here's a list of some of the best python libraries for natural language processing. Natural Language Toolkit (nltk) Covers all the basic functions and NLP tools ...
skillsmuggler's user avatar
2 votes
Accepted

When should I use feature learning as opposed to feature engineering?

manual feature engineering started becoming obsolete That is wrong. Any suggestion on when to use manual feature engineering, feature learning or a combination of the two? Deep learning is ...
Martin Thoma's user avatar
  • 1,055
2 votes

Is text preprocessing really all that necessary for NLP?

It all depends on the quality of data. Due to old rule "Garbage in, garbage out" link , if you have bad quality data(data redundancy, unstructured data, too much memory, etc) your results won't be ...
fuwiak's user avatar
  • 143
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 ...
nbro's user avatar
  • 41k
2 votes

How should I deal with variable input sizes for a neural network classifier?

It is much simpler to process the data in a different way. Since you're using temporal data a common practice is to define a priori a minimum time-step, usually called $\textit{granularity}$, which ...
Edoardo Guerriero's user avatar
2 votes

Is it recommended to remove stop words before named entity recognition?

It depends how you recognise the entities. If you do a simple gazetteer lookup, then it could be faster, as you have fewer tokens to deal with. However, if you use contextual rules, then stop words ...
Oliver Mason's user avatar
  • 5,397
2 votes

How to automatically detect and correct false information in columnar data?

While I can see that there are some heuristics that can tell you whether an entry is 'weird', I don't see any way that you can correct this. Where would you get a correct value from? I would perhaps ...
Oliver Mason's user avatar
  • 5,397
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 ...
nbro's user avatar
  • 41k
2 votes

Are my steps correct for a proper classification of a sick brain?

It looks like everything you want is available with the Deep Learning Toolkit (DLTK) for Medical Imaging There is also a blog: An Introduction to Biomedical Image Analysis with TensorFlow and DLTK ...
Brian O'Donnell's user avatar

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