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I have a very basic question. If I have two columns of data:

"Students": ["John", "John", "Roger", "Eve", "John"]
"Sex": ["M", "M", "M", "F", "M"]

can I use different encoding for each column? E.g. frequency encoding for students and binary for sex?Thank you for your answer. If you have tip for basic readings on this, it would be appreciated.

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If your response variable is sex then you meed to make it binary, otherwise I do not see a problem I think.

While the amount and sophistication of AI tools has taken a sharp upward turn, there's one particular type of tool I tried to find but failed: one that would change the facial expression in a photograph or other still image. I found some toy-like phone apps with very limited sets. The best more professional tool I was able to find was Photoshop's neural filters. They were introduced already a couple of years ago, so one would think more advanced specialized tools for this purpose would exist already. Are there such tools? Did my google-fu just fail?

I have a (seemingly) simple question concerning systematic studies for classification problems. Is there any literature (books, papers) describing an approach for systematic studies on classifiers, such as varying the size of the training sample, number of input variables, size of the correlation between input variables and classes on simulated data, type of classifier, configuration of parameters of the algorithm etc.?

The goal is to prove the robustness and limitations of the method before training on real data. While I have a good feeling of what can and should be done, I want to point a beginner in the right direction for a project without doing all the hard work myself.

I am trying to create a GAN with RNNs. Therefore I'm trying to create stacked GRU-Cells which get fed the random input. I implemented it as follows:

def build_generator():
    inputs = keras.Input(shape=[LATENT_SHAPE])
    cell = keras.layers.StackedRNNCells([keras.layers.GRUCell(64, activation = 'tanh') for _ in range(7)])
    rnn = keras.layers.RNN(cell, return_sequences=True)
    x = rnn(inputs)
    return keras.models.Model(inputs, x)

However everytime I try to call the method, I do get the following error:

Error

I have found basically the same implementation for StackedRNNCells in the second to newest push from TimeGAN. Yet for me I get the error, I don't know how to fix.

Welp, it seemed like the problem was, that the inputs need to be defined as 2-dimensional with the sequence length as the first parameter. I thought one would give the RNN only 1 dimension of latent noise and get the sequence through reiterating it trough the RNN.

Somewhat more limited than your question, but I know two such papers: "Tunability: Importance of Hyperparameters of Machine Learning Algorithms" P. Probst et al., and "Hyperparameters and tuning strategies for random forest" P. Probst et al.

Both are on Arxiv. First one concerns tunability of multiple ML algorithms, i.e. how sensitive are they in general to hyperparameter choice. Second one delves deeper into the same area, but specifically for random forests, gathering results from many other works. Using those ideas, I was able to dramatically decrease the computational resources for tuning by better designing hyperparameter grids.

Yes, you can. Variables in tabular learning are (in general) independent in terms of preprocessing. In fact, in most cases you will perform such different preprocessings, e.g. one-hot + SVD for high cardinality categorical variables, binary encoding for simple binary choices, integer encoding for ordinal variables.

Ball park conceptual number - how long does training take for AI tasks using medical volumetric data? (for example, something along the lines of training for automated segmentation of an organ using 100 CT studies). Are we talking hours? Days? Weeks?

I'm new to ML and I will need a better GPU (and a PSU and maybe a bigger case), and the amount I would be willing to invest depends on how much of a difference it would make in practice. I figure I can get a used RTX 3090 installed for about $1000 or a new RTX 4090 for about $2000, and if training correlates with AI benchmarks, then it looks like a task that takes 1 day for an A100 GPU would take 1.1 days with an RTX 4090 and 1.7 days with an RTX 3090. If the extra $1k reduces the time by weeks or days, then it should eventually be worth the cost. If it reduces the time by hours or minutes, then it's probably not worth the cost.

Thanks!

Discrete features as training data:

Say I am using SQL table rows as training data input for a deep neural net classifier. One of the columns contains a number from 1-5 representing a discrete value, say type of computer connection. It could be wifi, mobile-data, LAN, etc. What would be the best way to represent as input features? Right now I'm thinking split into a five dimensional vector, one for each possible value. Then pass 0 or 1 depending on whether a given feature is selected. I'm worried that including the range of values as a single vector would lead to messed up learning since one discrete value doesn't have any meaningful closeness to it's nearest discrete neighbor.

How do I find someone who has access to medical imaging models? I have found a couple open source resources but not sure if I have the skills/time to install the code. Specifically, I would like machine learning to analyze the scans I have of my left eye. I have a rare eye condition that has so far been deemed idiopathic.

Your suggested approach is the correct one and is called "one-hot encoding". Your thinking about why an embedding (single learned value) is inappropriate is also accurate.

Formatting lat/lng data for neural net feature input:

I've got latitude/longitude columns in a sql table that I'd like to add as features for a neural net classifier model. In terms of formatting for input, I plan to normalize latitude values to a range between 0-1, with 0 mapping to the largest possible negative lat value, and 1 mapping to the largest possible positive lat value. Then do the same for longitude, and pass them in as separate features.

Does that seem like a reasonable approach? Any other tricks I should know?

GPTZero claims to measure the perplexity of a sample of text. Am I missing something or is that a complete scam? You can’t measure perplexity without access to the model logits, which aren’t available for GPT-3.

You could guess what the logits would be by gathering text samples but there’s no way a pet project could gather enough data to accurately estimate conditional probabilities.

Can you adjust gantry length in a claw machine?

I’m sorry if this is dumb but I can’t seem to find this anywhere. I know absolutely nothing about the parts inside a claw machine other than the names. I have a cabinet but I am unable to find a gantry the exact size. Do I need a new cabinet or can something be done? Thanks!

I don't know your application but, if lat/lon don't work very well, you could also try something like geohashing.

Something that's weird about longitude is that it loops. you might have weird things at the boundary. It's also odd that the distance between two points is also a function of latitude.

Hi! I am working on a clustering project on a dataset that has some numerical variables, and one categorical variable with very high cardinality (~150 values). I was thinking if it is possible to create an embedding for that feature, after one-hot encoding (ohe) it. I was initially thinking of running an autoencoder on the 150 dummy features that result from the ohe, but then I thought that it may not make sense as they are all uncorrelated (mutually exclusive). What do you think about this?
On the same line, I think that applying PCA is likely wrong. What would you suggest to find a latent representation of that variable? One other idea was: use the 15p dummy ohe columns to train a NN for some classification task, including an embedding layer, and then use that layer as low-dimensional representation... does it make any sense? Thank you in advance!

That's a good point about longitude looping. I hadn't thought about that. I'm designing a classifier, and would like to include geographic location as one of the input variables.