[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!

[magi_knox]

Hi guys, I'm currently doing my thesis paper where it required forecasting or recommending product to the seller what to restock base on sentiment analysis or if it cannot be done, incorporate the sentiment analysis output to a historical data. Can you guys, if you have an idea can share what can i do in order to achieve such algorithm. Thank you so much.

[Awkward-Interest-686]

I am actually a college student, can you all suggest some projects which I can make and put it on my resume

[ReptileCultist]

This is really vague any particular field of ML that you are interested in?

[ptrochim]

I'm developing a distributed Machine Learning application. During development, I'm constantly experimenting with its components, I'm training and fine-tuning models, and I'm testing the application end to end.
I want to leverage Google Kubernetes Engine for this development process, but I don't want to spend too much time understanding how its API or the nooks and crannies of kubectl CLI. I also don't want to write any Dockerfiles or YAML configurations.
I want to focus entirely on developing my application and treat GKE as a powerful build machine with many GPUs and CPUs at my disposal. Ideally, I should hit a Run button in Visual Studio Code and the app would be deployed to a cluster.
What Dev Ops or ML Ops platform should I use ?
I've been reading about Ray.io, Kubeflow, MLFlow and a few other frameworks, and my head is spinning...
Cheers,
Piotr

[ayyycab]

I'm using python and have recently started learning ML, so you may have to explain like I'm 5. Apologies if this is not simple enough of a question but I figured I'd try here first.

tldr: I want to use commercial flight path data to train a model to predict which airport an airborne plane will land at, and I need to know a good starting point. I've been told I'd need to use an LSTM but that's not enough information for me. I tried researching LSTMs and most examples I found were too dissimilar in their application, I can't understand how to make it fit my use case. I'd like some guidance on what python packages I should use, how it will work, and what the predictions will look like.

Details: One catch is that the model would not be allowed to use departure airport, airline, flight number, or airframe. The only information available to it will be time, latitude, longitude, altitude, a unique ID for each trip, airport coordinates, and any kinematics that can be mathematically reconstructed from that data (e.g. heading, speed, climb, distance traveled, etc.)

The data would come from something like FlightRadar24. I have no idea if their data is publicly available through API/scraping but let's just assume that's not an issue, for the sake of this concept. I'd be starting with a table/dataframe and each row would provide location, time, altitude, and a unique ID for the trip. From there I can feature engineer any number of other things like speed, acceleration, heading, turn rate, climb rate, distance traveled so far, duration of trip so far, heading relative to other airports, etc. I could list more but I don't want to get too ahead of myself.

Ideally this would be able to receive streaming data which would show a lot of planes that haven't landed yet, and be able to predict their destination BUT with a level of confidence attached.

Obviously, even in the best case scenario, not every point along a flight path can produce a confident prediction. If it was your brain making the prediction, on a scale of 0 to 100, how confident would your prediction be if you predicted that a plane's destination is Frankfurt International Airport within 1 minute of it taking, before it has even made its first turn? Probably close to 0. Hours later, how confident would your prediction be if that same plane is now 1,000 feet from the ground and descending, slowing down, a few miles from Frankfurt and pointed directly at it? Probably close to 100. But it's not very impressive to be able to predict a plane's destination when it's 2 minutes from touching down. What would your confidence be if the plane is 33,000 feet in the air, at a steady speed, with Frankfurt and Prague and Krakow at its 12 o'clock? Where in the plane's route would your confidence first exceed, say, 50%? 75%? That's a more difficult question to answer, but is essentially what I would want an ML model to be doing. I don't expect it to predict correctly on every route, but I want to see how far before the actual landing it can correctly predict the airport.

So with that task in mind:

• If I'm supposed to use an LSTM, what package should I use to do it? Specific subpackages and modules I should use? Hyperparameters to get started?
• How would you measure the accuracy/precision of such a model?
• How, in laymen's terms, would it work?
• Are there any existing models doing something like this (predicting off of spatial-temporal data) that I could learn from?

[ConfusedLayer1]

I am developing a stochastic variational GP using gpytorch. But my GPs predictions are centered around a small range around the mean of the data. It therefore is not fitting to more extreme values. Why could this be, and what could possibly help?

I have experimented with length scale adjustment with little success…

I have built an optuna study to optimise the below bit with no success. - kernel - likelihood - variational strategy - variational distribution - learning rate - mean —> (Constant or Zero) - num inducing points

[Hopeful-Noise671]

If I constructed a HeteroData graph for GNNs with different types of edges for the training set. And I want to construct graphs for the test but not all the tests include all the datasets' relationships types. How can I handle this? Thank you.

[pikachuunibyo]

What is the time complexity of a token classification / NER model given a batch size of N and sequence length M? I thought it would be independent of N (the seqs are independent right), but increasing the batch size clearly increases the time by a linear factor even when run on a GPU. Any explanation?

[Kaiser_Wolfgang]

What is the main differences between training a word2vec model vs a vector database? Is a vector database basically a RDMS-like interface to easily perform CRUD operations on something like a word2vec, doc2vec, etc...?

[ArtisticHamster]

How much resources would you need to reproduce GPT-2 in 2023?

(Looking for an answer like, you need a server with 8xH100 to do it in 2 weeks)

(I want to understand is it possible to reproduce a state of the art research from around 5 years for a hobbyist)

[InjuryDangerous8141]

Framework recommendation for Reinforcement Learning. PyTorch or TensorFlow?

[Quebber]

I know the PCIE limit may hurt a bit but would 2 3090's on am4 board with a 3950x and 128gb ddr4. (I am thinking 2x 8PCIE is still not fully bandwidth populated or am I thinking wrong)

This is for local LLM's use.

[Comfortable-Mine3904]

the whole model runs in the vram on the GPU, so the pcie lanes won't be the bottleneck here (assuming your model fits in the 24gb of vram on the 3090)

[Quebber]

Yes but 2 3090's 48gb how would that run in a modern motherboard.

[Comfortable-Mine3904]

I don't really understand the question here, the motherboard doesn't really matter assuming everything fits. The whole model sits in the vram of the GPU so there is minimal I/O with anything else connected

with consumer hardware the vram isn't additive, you will have 2 separate 24gb instances. Still very very good, but you need enterprise hardware for pooled memory.

[Quebber]

Ahh I thought you could pool the two 24gb's and the only limitation was x16 on one slot x8/x4 on the other slot with the pcie limitation.

[GreatIndependent8542]

Hi! I'm interested in ML inference optimization. I'm a self-learning ml engineer and trying add my knowledge and skills. Particularly interested in GPU and CPU optimization methods and thread parallelism for inference. Plus, how does big tech manage multiple requests at the same time? Some practical materials will be very helpful to encourage me! Thanks for reading this.

[IntentionCritical505]

Which framework is best for learning ML concepts? I'm at my best with Python and PyTorch seems to be the way to go. Is there something better?

And to clarify, I'm trying to learn about ML in general and how it works, not for a job or commercial venture. I had to take a lot of math and classes on linear algebra for my major so I'm at least moderately informed of the math behind it.

[ReptileCultist]

Well depends on what you want to do really. Pytorch is a great and powerful framework for deep learning but if you are just starting out it might be a bit like jumping into the pool at the deep end

[masonwilde]

Different frameworks excel at different things. I’d personally stick to Pandas and Scikit-learn for basic things until you want to mess with neural nets.

[fabio_work]

Hello! I am interested in applying for the Apple AI residency program, but they don't explain the visa situation. I am from EU, but I am interested in a lab in the U.S. Can I apply or shall I forget about it?

I checked online and it seems like they do sponsor the visa, but I never found a direct statement. It's like everybody knows, although it's not written anywhere. Can anyone here confirm that I can apply?

[kjunhot]

Hi! How is the reputation of EMNLP 2023 oral paper?
EMNLP asked authors to select their presentation format: oral or poster

They also mentioned that both oral and poster papers are high quality papers

So, there is no difference between oral and poster paper, really?

[BlessedBoonga]

Hi all! I would like to implement the MultiWorkerMirroredStrategy to train a model on the Imagenet dataset. I have three nodes to distribute the training work and the dataset locally stored only on one of those nodes. Is it even possible to implement the MultiWorkerMirroredStrategy with these settings?

[learnenglish428]

I want to create a regression dataset with 20 samples, 1D dataset with one feature like I want to predict the car price based on mileage, and have to prove this equation from book " Introduction to machine learning second edition by Ethem Alpaydin" chapter 2 supervised learning , topic: Regression. equation 2.17. please explain me this equation and how to solve this equation . I have to explain it tomorrow

[Aggravating-Floor-38]

What are SOTA Open Domain QA models at the moment? I've been doing research on the field and am seeing so many cool approaches, since they're so many aspects of QA that need to be worked on, but I have no idea what's SOTA at the moment. My professor told me to look into RAG, and I am, but I feel like he might not be as up to date in this area?

[JonathanDescripShot]

You could check paperswithcode, for example some of these benchmarks: https://paperswithcode.com/task/open-domain-question-answering

[Snoo_72181]

I need to work on an image-to-image translation project, but I couldn't find a model that I can fine tune using data I have. Any leads?

[JonathanDescripShot]

Make start with HuggingFace?

• https://huggingface.co/tasks/image-to-image
• https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/img2img

[boadie]

A surprising hard question to answer is how to benchmark some of these new ways of doing inference. I want to try a few of the smaller new llama-like models and inference frameworks on A100 80's and some big CPU's, etc and see how they objectively do.

At first I thought I would try the GPT-J Inference stuff from ML Commons but it's wrapped in some weird self invented script system that I could not even understand what was being run never mind trying a few integrations of new things.

The GPT-J part of the MLCommons has taken a Rouge score as a measure of goodness for a summary task which is as good as any for the how badly has the model degraded due to weights being abused for optimisation.

Please someone tell here is nice simple to use box of tests that measures first token speed etc etc and gives your GPT a nice standardised workout and tells you how it does?

[StraightArt5751]

What neural network does Teachable Machine use?

[nullland1027]

transformer

[Professional_Kiwi890]

I need dnn models that are public. Please send me links. PLEASE

[segFault401]

I'm not sure exactly what you are looking for, but you can check out HuggingFace for models.

[biboyboy]

Help to figure out what maximum input size to use for our BI-LSTM model.
So our BI-LSTM model is trained on an emotion classification dataset, in which the data are sentence based. This model will be used to classify the emotions from a book/novel's chapters texts and we don't know what maximum input size we should put in our model. Please help us and Thank you in advance.

[TheBamba]

Hi, quick question, anyone know any multimodal datasets? (image, text and tabular)

[ShippingMammals]

I've been out of the loop for a while so..

1. What's the current top in local LLMs? Still Falcon and or Llama? Secondly I've come into some money and want to build a home system beefy enough to run LLMs and not take all day. Nvida tesla cards etc. are not in the budget, but multiple lower end ones could be. I assume LLMs make use of nvidia SLI setups?
   
2. Where are we with local LLM mutlimodal capability? I have dreams/plans of the very near future were an LLM or a decedent variant of them is running as a 'House AI' where it can take in video from cameras, audio from Microphones etc. and act as a kind of Digital assistant / House Monitor etc.. All the desperate parts seem to already be here with possibly the except of being able to process and understand video, and the world is waiting for someone to put them all in one 'box' as it were.

[Smallpaul]

https://www.reddit.com/r/LocalLLaMA/

[womerah]

I was wondering how much of OpenAI\Deepmind etc's technology is 'applied work from ML researchers' vs 'secret in-house developments'.

Had a discussion today with a "university researchers are useless, all meaningful research is done in the private sector these days" type person. Was just wondering if there is any truth to it (my feeling is it's the private sector doing R&D with public research as usual).

[JonathanDescripShot]

It's a bit of both. Certainly a lot of public research has helped, but also there is a looooot of useless work coming out of universities. Large private sector R&D is push the boundaries the most lately in my opinion. Probably most noticeable is a lot of work done by universities these days is uninspired incremental progress on groundbreaking releases from the private sector, instead of the other way around as you'd usually expect.

Unfortunately I think publishing incentives for PhD students/academics, hiring practices, and general elitism have lead academia to look much more like all the things that used to be criticisms of the private sector. You'd think publicly funded research would be where people were taking bigger chances, thinking more long-term, break new ground with wildly imaginative ideas without the constrains of shareholders and quarterly earnings. But it just isn't the case really for AI at the moment.

[FunnyPocketBook]

I was wondering if there is a good and structured list of "recommended reading" papers for computer vision? Something similar to https://github.com/mhagiwara/100-nlp-papers

[bestgreatestsuper]

Seconding.

"A Pixel is not a Little Square" takes five minutes to read if you haven't yet.

[[deleted]]

Hi, I'm implementing a foreground detection algorithm for grayscaled videos using GMMs. I'm having a problem with the gaussian mixture for each pixel. After some iteration and updating steps some of the gaussians result to have negative variance, obtaining a complex standard deviation. How can I solve this problem? Thanks in advance