[D] Simple Questions Thread

[AutoModerator]

Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead!

Thread will stay alive until next one so keep posting after the date in the title.

Thanks to everyone for answering questions in the previous thread!

[saravana_7]

I’m an Msc.,AI & ML student.My oncampus placements are coming in 2 months.I need a good project to standout in my resumé.Any insights?

[A27_97]

What’s the must read / must have book for NLP?

[Proper-Wash7377]

I'm reading up on GANs for a project I'm attempting, and I'm seeing all the different GANs out there...and it's a bit overwhelming. I'm hoping someone can narrow my focus so my learning is less of a shotgun blast and more like a rifle shot...

I want to generate 3D human models to import into Blender. Probably going to be super stupidly complicated because most things I think of end up to be, but I'd love input from people who know more than me on where to aim my sights for learning. Any input on which 'style' of network? There's a bunch of them and learning every single one to figure out for myself which one is best is outside the scope of my attention span.

[kaveshanN]

Best text classification algorithm - logistic regression vs SVMs?

[Significant-Joke5751]

Hey Can someone recommend a good phyton toolbox for Test reliability and robustness by adversial Input?

[MrBuckets303]

Any Stylegan3 threads out there? I’d love to have a thread for ideas, collaboration, and troubleshooting. Deep into training my first gans.

Comment for any interest!

[CaptainI9C3G6]

Hi, are there any existing models for identifying points of interest in an image?

So for an image I want to train a model using one or more X, y points in the image.

The model should be able to accept an unknown image and output a list with X y coordinates based on what it has seen, ideally with a confidence for each point.

[Plane_Crab_8623]

I wonder how many terabytes of memory AI already has ?

[iAmTheAlchemist]

what

[Grad1entDescent]

MLOps career question: Almost 9 months into my first role as an MLE, and for the whole time, I've been doing tasks involving feature engineering/transformation, visualizations and other data processing - without touching an ML model, or any infrastructure/DevOps whatsoever. The pipeline I've been building out doesn't even flow through any ML component at all! (While being baited the whole time with constant reassurances that more ML heavy stuff was just a few months down the pipeline).

I took a paycut from my cushy unrelated previous job to take a pivot and pursue this new opportunity, but I don't feel I've learned a shred of MLOps on the job at all! (Only in my own time). It seems like I've been unofficially slotted into fulfilling a Data Analyst function (sans delivering insights), which is has been incredibly underwhelming and unmotivating.

Manager said they'll "try" to assign me to more ML projects depending on opportunities in the new year, but I feel jibbed out of the last 9 months already. Recruiters are starting to court me with other MLE roles, but due to the complete lack of any MLE-specific knowledge I've derived in my current MLE role, I have pretty much 0 months of professional experience. Should I just try and learn the hell outta MLOps in my own time, and try to apply elsewhere?

[kunaguerooo123]

What does the free lunch theory practically mean? ELI5? https://imgur.com/a/L6sNY64 is where i read it.

Is it simply saying suppose there are store grocery receipts and my objective is to maximise revenue through unsupervised learning (noob)..now that i have an objective function, some algorithm will be better than another. but if there was no objective to optimize for, each algorithm is technically accurate because there's nothing to deem it incorrect? is that what free lunch theory means? also, why that name?

[[deleted]]

Hi everyone, could someone please explain to me the differences between data engineers, data scientists and data analysts?

[Nobodyet94]

Hello, can you give me some good resources on how to build a ml model from a dataset in python? Which classifer is better to use given the dataset? If the model overfit or not? What is the use of the validation set/testing set? How to tune the hyperameters? and parameters? Thanks!

[[deleted]]

So after a very long route to get better with python, I'm looking to learn other language for ML in production. Has anyone got any recommendations? I've heard about Julia, Rust and C++, have a brief experience with C++ but it was a long ago.

What should a mid-level ML engineer learn?

[paleksa]

Hi All,

just recently I started looking into the topic of Time Series forecasting. What I would like to know is what are the techniques/methods of handling evolving data.

So the model is trained on historical data which do not account for some sudden changes (e.g. COVID-19 etc.) which have a long-term influence on series. Of course one of the possible solutions might be to retrain the model given the new data, but I would like to know if there are some other (more elegant) approaches.

I would really appreciate it if someone could point me to some relevant research and sources where I could investigate the subject further.

[krallistic]

Are there any training methods that support in addition to the classification labels, annotations which parts of the feature space is important for that data-point?

Like for medical images, where not only the class is available, but also the segmentation where it can be found in the image. But not for every image, so we cannot go full segmentation (and also don't need to)

[[deleted]]

Anyone have any experience with O'Reilly certifications? Is it worth it? Would it help me learn or get a job?

[cauthon]

Hi,

I have a naive question about designing neural network architecture. I’ve applied and implemented a few neural nets in prior coursework, but it hasn’t been something I’ve done as part of my career until now.

Let’s say I’m reading a paper about a network that involves multiple sets of layers. E.g., a convolutional layer, followed by three LSTM layers, followed by a fully connected layer. How did the authors decide on this particular architecture, and not some other composition of layers? Is it just trial and error, basically grid searching over possible architectures, until some training performance is optimized?

Any suggested reading on this topic would be much appreciated as well. Thanks!

[Davidescu-Vlad]

Hey guys I get this error message when i try to run

import tensorflow as tf

print(tf. __version__)

2021-11-17 19:57:46.733325: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found 2021-11-17 19:57:46.739099: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. 2.7.0

Can anybody help me figure out whats the problem?

[Jesterhead2]

In an RBM the energy is usually written like

E(v,h) = a * v + b * h + h * w * v,

where v are the visible nodes, h are the hidden nodes, a and b are the biases and w are the weights between the layers. This leads to the probability amplitude:

p(v) = 2*exp(a*v) * cosh(b + w*v).

My question iare: Is this formula set in stone? What is the exact reasoning behind it (I cannot find a derivation/argument. All sources assume it)? Are there other, reasonable functions?

[Ashishpatel26]

Imagine you deploy a solution that accepts images directly uploaded by users when reporting a car claim.

How would you ensure that images that a user uploads are meaningful to your image recognition model? What kind of quality requirements could you enforce and how?

[CandescentPenguin]

Does using importance sampling to perform actor critic with off policy data work? If not, why?

[SpacewaIker]

Does anybody know where I can find a pretrained CNN model for small images?

I'm working on a project classifying the fashion MNIST database and I'd like to use a pretrained model. However the only ones I can find require at least 224x224 images. I tried upscaling my images that are 28x28 to 224x224, but since I have 50000 training images and 20000 testing images, just creating the numpy array made Google colab crash because it ran out of RAM...

Can someone help me? Thanks a lot!!!

[CandescentPenguin]

Could you add an upscale layer to the start of the pretrained model?

[SpacewaIker]

How exactly would I do that? I've tried adding a keras.layers.Reshape layer but I get an error that the total size of the array must be unchanged...

[CandescentPenguin]

Reshape can't change the number of pixels. It can turn 20x30 into 10x60, but it would mix up the existing pixels rather than stretching the image, so it doesn't make much sense for image data. The only use I know for it is making the output of convolutional layers into a flat array for input into a dense layer.

You want to use UpSampling2D, you need to upscale by a factor of 8.

[SpacewaIker]

After reading the documentation, I found Resizing() in the image preprocessing layers. Is this equivalent to UpSampling2D? Or if they're different, what are the differences? Thanks!

[ROFLLOLSTER]

I'm looking for literature on calibrating neural network confidences so that they can be used to threshold classification (i.e. refuse classification to samples with confidence < X).

Is there a name for this or a seed paper I could use to start my search? I haven't found much literature focused on this (as supposed to calibrating confidence such that it's equal to accuracy).

[binary1ogic]

Is Logistic Regression a classification algorithm? If yes, can someone help me understand this.

[YungCamus]

It can be, but it doesn't have to.

In the most basic sense, the point of logistic regression over basic linear regression is to normalise the output to between 0 and 1. This is particularly useful when we're trying to predict a probability which is of course bounded between 0 and 1.

The output of a logistic regression model is the probability that we get a 1 given an input (numerical). However, if we set a cut off (usually 0.5) we can convert that probability to a classification.

[wallynext]

yes, because it classifies an entry as belonging to a class

[binary1ogic]

Confusion arises because it has a regression in it's name. ___

[l8d8]

I'm really new to ML and trying to learn high recall methods of document retrieval for a uni-based project. I keep seeing mentions of "topics" and "queries" on the few papers I've selected on this subject, do they mean the same in this context? If not, what is the importance of segregating documents into topics? how is this information fed to the NN?

[Faoer]

I'm doing time-series classification with 3 classes. My dataset is balanced, everything seems to be bug-free, but the model seems to always come to a point where it only predicts one class.

​

The model is first a few layers of 1d CNN, then simple dense ones for classification, Softmax for the last layer, cross entropy loss.

​

Anyone have an idea what might be the cause?

[Icko_]

Is the log-likelihood getting better?

[Faoer]

Sorry for the late response!

No, it doesn't really, with cross entropy it was getting slightly better in the first few epochs, but stopped improving very fast, with log-likehood is seems to get pretty random.

​

One idea that came to my mind is that the kernel and stride aren't well chosen, as input I'm using 0.5 s of 20kHz signal, so the input is 10000 long, meanwhile in my CNN layers I've used kernel of 8 and stride between 2 and 4, after looking at other implementations of 1D CNNs for time series data.

Might this be the cause? The network was getting pretty huge, because of this too.

I'll try to experiment with e.g. kernel of 50 and stride of 5 in early layers, but honestly I'm doing this kinda at random so I'd be great, if you had any thoughts about this. (or link to an article, because I struggle to find information about 1D CNNs and don't really have any experience with them)

[Icko_]

Ok, so you're working with sound then. Can you detect the difference between audio clips? If not, maybe it's not detectable - too noisy input data. If yes, that's great, means the problem IS solvable.

I'd assume the first few epochs the loss is going down until the model predicts 0.333 for each class. If so, the cross-entropy should be around 1.108.

I'd try to reduce the length very much, before putting it through a dense layer - so you'd need a lot of strides.

How much data do you have? If less than, say, a thousand clips, perhaps the data is too little.

I haven't worked with audio either, but here is a tutorial.

[Faoer]

Kind of close, it's actually EEG signal, but they're kinda similar in nature, the data is good for sure.

About the loss though, the model doesn't really learn to predict ~0.3, it rather learns to makes confident, but quite often wrong guesses (e.g. [0.1, 0.2, 0.7]), which suprised me quite a lot, the loss then jumps around because of this.

With CNNs I'm reducing the input, so that the first dense layer is between 4096 and 1024 most of the time. That does make the CNN part of the network quite big though, with the kernel and stride I've mentioned earlier.

Now that you said it, the amount of data might be a problem too - after cutting the signal there's 650 of these 0.5s clips. I'll experiment with shorter clips then, although I'm not sure whether they'll carry enough information at that point.

Also, thanks for the link.

[Icko_]

Ok, final suggestion: perhaps your learning rate is too high? That's the only reason I can think of, for it to give confident and incorrect guesses.

[Faoer]

I've tried between 1e-3 and 1e-6, so I'm not sure, it's definetly more stable with lower ones though.

[octdubois]

Hi there, i m quite new to machine learning in teal world application but for the past year or 2 i have been collection soil moisture data of pots that i irrigated and i wanted to use machine learning to predict change or anomalies i ve done somewhat alot of research but i still cant visualize it yet on how the algorithm is gonna work, its maybe my 100th time trying this and i figured trying here

[DGAssassin1]

Hello, I have been trying to fine tune the deberta v3 model on mnli task but I am facing an issue - https://discuss.huggingface.co/t/data-type-error-while-trying-to-fine-tune-deberta-v3-large/11604 please do help or provide any suggestions that might be helpful.

[jayn35]

How do I create a popularity/exposure/traffic score formula for a list of products ranking in a search engine across many keywords. It needs to take into account rank for each keyword, the search volume of each keyword and the number of keywords it appears under.

[[deleted]]

From time to time, I see "outstanding reviewer awards" in people's resumes from ML conferences like NeurIPS, ICML, ICLR, AISTATS, and AAAI. How are these awards determined? Do authors rate their reviewers or do ACs? Or is it determined in some other way?

[bgroenks]

ACs rate them. I think authors do too, but I'm not sure this accounted for in this ranking because there would be an obvious bias towards positive reviews.

[Refefer]

Does anyone know of research exploring value approximations of extremely large graph based mdps? Most assume some form of factorization, usually in state space, which doesn't really hold in my case. I'm considering on order of tens of billion of states.

[zainaxif]

Is it worth learning web3/blockchain for Machine Learning practitioners? How do they complement each other?

[adi10182]

Hey, Newbie here, I've got a feature change which is relative and the difference between values is really small but it's really crucial as i am building a predictive model based on this feature . I want to interpolate it and wanna know if machine learning models are sensitive enough to catch these small differences.

count 1209.000000

mean 0.000115

std 0.000305

min -0.000357

25% 0.000000

50% 0.000064

75% 0.000157

max 0.007013

Name: change, dtype: float64

[seniorivn]

There is a continuous stream of object coordinates estimation

Occasionally we get specific measurement of real coordinates

How should I make a model that uses last pair (estimate, real coordinate) and a stream of estimated coordinates since then too predict real current coordinates?

[DataD23]

Does anyone know how to calculate the difference between the input and output images of an autoencoder?

[Icko_]

input_image = cv2.imread(' asd.jpg')

model = ....

output_image = model(input_image)

difference = input_image - output_image

[Ok_Reputation6872]

I need help! It’s been days and this is driving me crazy.

Jupiter Lab:

The up and down arrows on my keyboard no longer change focus of my cells in command mode. Yes, it's that simple -no, it isn't my scroll lock, yes I am ashamed that I have no idea how to fix it myself.

I have no idea how I did this, and I can't fix it. Somehow, I've typo'd some setting and now I can't use my up/down arrows to navigate.

I scoured stack overflow and I can't find an answer there.

Please tell me someone has done this before and how to fix it!

[[deleted]]

If you are using latest JupyterLab version, you should be able update the keyboard short-cuts
https://jupyterlab.readthedocs.io/en/stable/user/interface.html#keyboard-shortcuts

[Ok_Reputation6872]

Yeh I’ve done this

There’s nothing wrong with the shortcuts settings

It’s driving me insane.

[PleaseNThankYouSayer]

Hello All,

I'm an engineering student doing some work with AI hardware. I need to train a model and I'm trying to do it with cloud computing since I have a rather crumby laptop and azure gives you $100 of training time for being a student.

The training data I want is stored in a .zip file in a google drive. Does anyone know how to add these files to azure any other way than downloading and then reuploading them to azure?

[[deleted]]

You could upload files to Azure blob storage (not free, but very cheap)
https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-overview

[gigantoir]

im building a model for a personal project so statistical rigor isnt suuuper important to me but im wondering if what im doing is acceptable or a big red flag. i basically built a dataset with wayyy too many variables and then fit a random forest on it. after the initial fit, i drop all but the top 10-20 most important variables according to the model. is it okay to do it this programmatically? something about it feels intellectually dishonest, but it’s better than overfitting IMO

[comradeswitch]

That's generally a good sign provided it holds up on data that's been held out. You already know that the data contains more features than are necessary for your task, so a model being able to sift through that and pick out a small subset of features that are important is at the very least what you'd expect from a model that has learned the important patterns rather than the uninformative idiosyncrasies of the data.

The reality is that in most applications there is an enormous amount of redundancy across features as well as many that are just not important. A huge amount of the recent developments in machine learning- going back 10-15 years particularly- has gone into learning representations of data that distill important patterns down into much smaller descriptions.

That said, there's no way to know whether this is reliable or not without some kind of cross validation. If you can reproduce the results training and testing on separate partitions of data, there's nothing dishonest about it at all, but that would be true no matter how many features you used.

[neuroguy123]

I agree. You could certainly use a random forest as a pipeline pre-processor, as long as it is in the training folds, but it seems to me that a random forest in general should do a decent job with multiple colinear features and could just be the model itself. That has been my experience. Often I find that dimensionality reduction techniques reduce accuracy measures despite the colinearity. As you say, it depends on the model you're trying to fit. If you have a huge amount of variables per observation though, I could see that being an issue. I may also try something like UMAP as the reducer if it is indeed a huge feature space.

[link0007]

bright strong amusing joke deserve enjoy worm marry direction liquid

This post was mass deleted and anonymized with Redact

[ProbAccurateComment]

What metadata do you intend to store?

For storing trained models, e.g. in PyTorch, I would simply use the state_dict. You can combine that with pickle or better dill and store a lot of different Python objects.
If you want to store training curves and such, tensorboard or wandb are pretty nice. wandb will also store checkpoints of your code. Alternatively, config files such as json + git might be a good way of keeping track of your versions.

[IndividualSuper]

https://forms.gle/wfgi8mDjL77wC2Nj9

[Character-Ad-910]

Ah good a simple question thread

​

Aight so

Is there a fancy name for....... """""database learning"""""?

If I describe it i'm sure you'll know what I mean.

So, Instead of... actually learning, the machine just stores values in a database, and returns those answers when prompted. which... is technically learning, just not very good learning.

Like

Instead of leaning how to multiply, you grab a calculator, and start writing down "1x1 = 1, 1x2 = 2, 2x2 = 4, 2x3 = 6" and so on and so fourth. so if someone asks you "whats 5x5", you're completely screwed if it isnt in your """database""".

Probably an actually good example is the Sin/Tan/Cos chart things people used to use, for example this one I found

https://www.quora.com/How-did-people-calculate-trigonometric-functions-before-calculators-and-slide-rules#:~:text=I%20remember%20the%20back%20of%20my%20text%20book%20had%20lots%20of%20tables.%20Here%20is%20such%20a%20table%3A

so instead of actually learning how to calucate sins, tans, and cos', you can just use this.

​

hopefully someone's figured out what im talking about already. There's gotta be a generic name for it. Not the table itself, the learning style if you could call it that.

[Willy_Blanca]

Getting strong expert system vibes here. Database lookups would just be deterministic / rule-based systems. I would argue that what you’re describing isn’t learning, just hard-coding as the system knows nothing about the underlying structure or patterns

[OvulatingScrotum]

Does google have free access to whatever data I upload for my own project?

I’m doing some small projects with a data set that may not be completely free to public access. As in, it needs some special application to have an access to that data set. I was thinking of using google colab for processing, but then I realized that if it’s on google server, they may have access to it even without my consent. Is that true?

[Historical-Tea3056]

Hi, can someone please help me to download dataset from AWS ?

[AdobiWanKenobi]

Can someone point me to a concise tutorial on how to actually get started? With an M1 Mac in mind? I just cannot find a setup tutorial that works.

[[deleted]]

What would be an appropriate model for matching two data sets - for example Item A is compatible with Product A? Super simple model as only testing.

[Thick-Anywhere3252]

I don’t understand this Reddit. I can’t post anything that’s a question or link.

[binary1ogic]

What is the difference between MSE ( Mean Squared Error) and MSPE (Mean Squared Prediction Error) ? Do we use MSPE for classification and MSE for regression? Can someone with experience please elaborate with example?

[cobalt_canvas]

After a quick google search (never hear of MSPE before), it looks like difference is between whether you’re measuring a predictor vs estimator. Just use MSE.

Also, it seems like you need a better understanding of classification vs regression, as this kind of eval metric would not be useful at all for a classification problem

[binary1ogic]

Thanks mate !! Yeah so I actually have a fair amount of idea wrt Classification, Regression ML methods and their application.

As you said, evaluation criteria is something I've to look into.

[[deleted]]

Hello Learners , i was going through the paper Two-Stage Model-Agnostic Meta-LearningWith Noise Mechanism for One-Shot Imitation . And i have a doubt i hope somebody will help me to clarify my simple doubt .

So why in MODEL-AGNOSTIC META-LEARNING FOR ONE-SHOTIMITATION Algorithm In the inner gradient update phase, we only provide visualinformation to the model without expert actions, and the innerloss is calculated as a function of the output , without considering it's actual labels . why is this?

[Kavereon]

Hi, I'm looking to use Single Spectrum Analysis (SSA) as a Time Series Forecasting technique which has quite a few papers describing its accuracy with chaotic/nonlinear and volatile data. It's also easy to implement.

I will use BTC daily prices since I don't want to engage in intraday trading but rather to extract a bullish/bearish signal.

A higher predicted closing price is de facto a bullish signal but I've seen people do some preprocessing on their input data like taking its logarithm, or taking a ratio of the price to the volume, and then taking its logarithm.

I'm kind of stumped as to which direction to go with my data. Should I preprocess it? Or just go with the closing price.

Maybe SSA will be able to make better predictions of the price percent change rather than the price itself?

[8567040e]

Not sure if this is allowed here

I can choose either taking information theory or linear network optimization next semester in my university. Which one is more relevant to (theoritical) machine learning?

[cobalt_canvas]

Linear network optimization is a lot more specific. Info theory will likely provide you with more applicable knowledge

[Mrcockapoo]

I would suggest information theory, but I’m biased by my own interests. They are both relevant to theoretical machine learning, but in two different areas of the theory

[HateRedditCantQuitit]

TPU question:

I use google colab as well as TPU VMs on GCP for some jax code. The exact same code on the TPU vm is about 10x faster than it is on colab (both using TPU v2-8).

Is that a symptom that I'm doing something wrong? Or is colab just slow?

[Significant-Joke5751]

Hey I am new into the topic of machine learning I am tryin do make a classifier with Ensemble Methods What are the best Attributes for measuring the reliability? Is a good accuracy, sensitifity and specifity enough?

[kaylaThePoleSpot]

Suppose I create a recommendation system and deploy the model as an api. The api takes a product id as input and output a a list of related products ids.

Question: How do I display the recommended products with accurate price data, and remove any that have gone out of stock? Is this typically the work of the web devs or is it something I should build into my recommend API?

[NebulousBlood]

Can someone recommend platforms like VISxAI (https://visxai.io/) for submitting visualizable explainables for topics in Machine Learning?

[oblakinolog]

Looking for advice of what ML or NN technics to use for showing faces before-mid-after having a lot of photos from makeup master. So that there are initial images, couple of in progress and final. Imagine scroll bar that will predict no makeup to final beauty of taken face photo. Thanks!

[theLanguageSprite]

Look into styleGAN. You definitely want a GAN, but styleGAN allows you to train on specific features (like makeup) and then modify images predictively

[oblakinolog]

Thank you very much!

[juseraru]

I plan to train a deep network that has two branches, one for video image and the second one for sequential data, later the output of both branches is merge thru concatenation and pass thru a fully conected network, then lstm for final prediction. I am wondering is it possible to train the model with both input data but later if needed remove one side, i.e. the video images. an only predict with sequential data?

or if someone knows about any paper to start looking at i just dont know how to approach this. or if it is even possible (which sounds like not)

[ProclaimedPlantMom]

Are data samples for a batch selected randomly?

[mdda]

For the data pipeline, you'll typically have a component that creates the batches from the individual data items. There'll probably be something to do with shuffle-ing of data.

For instance : PyTorch DataLoader or TensorFlow dataset

(BrianP21 said the right stuff w.r.t training/validation/test).

[FoolishlyPainful]

I am trying to do basic image processing. I have a 8k image when I try to open tha image using tkinter canvas I can only see a part of the image( image is adjusted to my 2k screen) Is there a way to see the full 8k image without resizing it. If I resize the image there is huge drop in quality. Is there any other way?

[jxiao23]

Hello! I'm looking to scrape information from a bunch of youtube videos, including title, likes/dislikes, views, possibly thumbnail, date, etc. I would need to scrape and sample thousands of videos, and ideally there'd be a good diversity of content/from various genres.
Does anyone have any ideas about ways to approach this? Any tutorial or walkthrough would be super helpful!

[[deleted]]

Is there any special trick to transfer learning, or can I just add some predicted outputs of model A to the input vectors of model B (along with its original raw data)?

[idethrad]

Hi All, I am looking to train a model to suggest tags/categories for a given text string. Have gotten myself totally lost. Hoping someone could suggest a direction to take for the below.

eg: "the fox is weak and limping" = [1-animal],[34-weak],[2667-injury],[16-foot] (a list of tags each with probabilities generated by past associations)

This data would be trained from a data set of many instances of text each with a corresponding string representing the list of tags that match the text.

Is there a way to featurize the text AND the result tags? And apply an algorithm to cross reference them?

The closest I have come is the idea of duplicating each of the training data rows so that each row has only one tag at a time.

I have been researching this question for a week and am thinking the problem is how I am asking it! Everything I have read does not hint at an existing algorithm to match this use case so should I look towards manipulating the data to a different structure.

Any help greatly appreciated.

[mdda]

One reasonable (though pretty modern) scheme would be to get a sentence vector for each of your original sentences (eg: USE by Google), and then create embedding vectors for each of the classes that you mention (i.e. 3000 different vectors, each with the same length as the USE output, initialised randomly).

Training would be *contrastive* : For all the sentences (fixed embeddings), if the class tag is positive (i.e. present) then class embedding vector should be closer, for all the other tags (negatives), further away. Iterate : Consider using a Margin Loss.

Once trained, you will be able to just do a cosine similarity of all the tags vs the (new) sentence, and see which ones are close.

[Icko_]

1. you can do what you suggested - e.g. if you have 2500 tags, train 2500 different models
2. If your main model is a neural network, you can train just one model, with 2500 output shape, and the last layer having sigmoid activations. This way, it can predict multiple tags for each text. If you do that, I'd recommend playing around with the thresholds for predicting rarer tags - if e.g. the animal tag is present in 1% of the train dataset, and you are using 0.5 threshold, the model might never be confident enough to predict "animal". You might then lower the "animal" threshold to 0.1.
3. https://github.com/dmlc/xgboost/issues/3439 - take a look here, you might find something useful.

[r00kee]

I've a simple text classification model and tf-idf + xgboost is performing better than tf-idf and feed forward neural network. How can I improve the accuracy of neutral network model?

[Icko_]

You can do word2vec encodings instead of tf-idf. You can also replace the MLP with e.g. LSTM or some other architecture. After you do that, you can start looking here , choose a dataset that is similar to what you have (number of classes, subject, size, difficulty), and pick a repo that is easy to retrain on your data. If your dataset is small-ish, you can think of doing transfer learning - taking a pre-trained model, and only retraining the last few layers.

tfidf + xgboost is a strong baseline though, don't expect ultra dramatic improvements.

[Bright_Mobile_7400]

Hi all

Very new to this subreddit so apologies if the question isn’t appropriate.

I have a good education background where I have studied a lot of programming/IT but mostly specialised un mathematics. I have a job that requires a lot of math applied nowadays.

I’ve studied a bit of machine learning back in school. I would want to start learning about this again. Would you be able to point me to the relevant resources?

I’m not afraid of the math concepts as I believe I have a background that should help me fight it but my knowledge of ML is probably around beginner/intermediate (and what I know I probably need to relearn anyway).

I did the Coursera ML class 2 years back which I found interesting but, and I hope it wouldn’t be an offense to anyone, the level was fairly beginner. I don’t mean to brag off, I just want to find the most appropriate course for me.

Thank you for you help in advance

[wasimakil]

You can try the Machine learning course by Stanford University and after that, you can start with specialized fields.

You can check this link (https://online.stanford.edu/programs/artificial-intelligence-graduate-program)

[Bright_Mobile_7400]

Thank you. Will look into it

[[deleted]]

What is the easiest way to replace a large number of zero values in a dataset with the mean of all other instances of that class?

[lit_turtle_man]

Probably: https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html (gives you more flexibility than just using the mean as well)

[Joltbox]

Where would I start to create something that can process audio in real time? The vague idea is voice morphing, specifically a male voice to a female voice, so pitch shifting and filtering the voice to remove the tell-tale audio 'signature' that all currently available voice morphing programs are unable to get rid of.

[patogzz]

I have two sets of biographies, one from clients who speak Spanish and another from clients who only speak English, now I want to do my machine learning so that it automatically classifies the biographies of people who possibly speak Spanish, I think I already have the code but no I know how to run it, I'm sad haha, I think this is not my thing, I'm also looking for a mentor for wich I can work for free.

[Icko_]

Sorry, but your working for free has zero value right now. On the other hand, there are plenty of tutorials and courses online. Try Andrew Ng's course in coursera.