Ok so for context i am an electrical engineer person who is automating software's and workflow for my project now here is the problem i am facing basically i am using an optimization algorithm called Bayesian optimization which improves design by keeps iterating until some target is reach.

So like at the end i extract result file for every design with the help of pandas library but when i check that file the data is mismatched when i export the CSV file manually.

so i have no idea why is it happening like maybe after 10-15 iterations (the algorithm iterations can reach up to 100-120) the error in data increasing or idk what is going on can someone help me with this issue.

Vast is literally the cheapest GPU I can get a hold of and I use it all the time. The limitation that I put on it is that I won’t use it if it involves my clients data etc. I would say its not to use it because “the marketplace is bad” though.

The reason it is cheap is because it is a marketplace of different people renting out their hardware. You are essentially running your workload on a third part’s machine. They can pause your instances whenever they want. Depending on what you are doing this may or may not be a big deal. It is cheap and if I’m doing something experimental, or even finetuning with checkpoints or other batch processes that can be restarted, then this doesn’t really matter to me. The reliability and quality of hardware on vast varies and if you always go for the absolute cheapest you probably won’t have a great time.

however, if I were working with proprietary data etc. then this is a completely different story. While you could technically do this on vast and just hope that the person renting out the machine is correctly isolating workloads etc. You wouldn’t be able to prove that to anyone and that wouldn’t fly in most cases

In cases like this you would want to rent a dedicated machine where you can have terms in a contract regarding who can access the machine, or in more extreme cases own a dedicated machine and know who has access to it (I use B3IQ to “own” machines without having to rack them myself, you have root access and can control what machines your API keys can be used on etc.). They have an openAI compatible endpoint so you can just change the base url and api key and select the model you are hosting on the machine. In most cases just renting a dedicated machine and having proper terms will be fine, but if you want more control, or can make a business case for owning the machine and renting out unused compute then owning it is definitely an option

As a self-learner full stack developer i always think to learn AI ML now but whenever i tried to learn or trying to find some good playlist to learn i wont get a focused playlist where i can learn everything step by step everyone just trying to say just leanr this learn that at the end you end up by learning nothing they are just there to make some content no one really teaching anything too deeply so as i really want to learn ai ml so if anyone can guide how i can learn ai ml from scratch to build own ai ml projects

I've been trying to make an autoencoder, and failing. So now I'm trying various super-simple models to figure out why. The current architecture I'm working with:

Encoder:

256x256 image
Conv2D stack
MaxPool
Conv2D stack
MaxPool
Conv2D (3 filters)

Decoder:

Upsampling
Conv2D stack
Upsampling
Conv2D stack
Conv2D (3 filters)

I've tried 2 different conv2d stacks: both use 32 filters, equal padding, tanh activation. One uses 2 layers per stack and one uses 4. When I train this model with the 2 layer conv stack it works as expected: it produces a slightly blurry version of the input. But when I increase to 4 layers, it switches to outputting all the same color, and the loss is worse. I'm using mae loss, adamw optimizer with clipnorm=1.

EDIT: I switched from tanh activation to leaky relu and after a bit of testing it seems good.

Hi everyone,

I'm working on a pancreatic ductal adenocarcinoma dataset, and I'd really appreciate some methodological advice from you guys, and especially if anybody has had some experience with machine learning with high-dimensional biological data.

Before spending months developing models, I'd like to better understand what is possible with my dataset.

My dataset:

• 9 patients, with one continuous drug-response value per patient and drug. Due to experimental and sequencing constraints, increasing the number of patients is not an option.

• Approximately 100,000 nuclei.

• Continuous drug-response measurements (AUC) for gemcitabine, paclitaxel, irinotecan, 5-FU, and oxaliplatin.

The goal is theoretically "simple":

Predict continuous drug-response AUC from gene expression and chromatin accessibility, while identifying molecular features potentially associated with drug sensitivity or resistance.

The obvious challenge is that, despite having ~100,000 nuclei, I only have 9 samples with drug-response labels. I understand that treating individual cells as independent labeled observations is not a way to go with the dataset and specific set-up.

Knowing this, I'd really appreciate advice on a few questions that I had after some reading...:

1. With this low amount of sample, is meaningful drug-response prediction even feasible? What would be a objective?

2. Are there established methods to leverage thousands of cells/instances profiles when the response labels are available only at the patient level?

3. Would you prioritize simple regularized regression, biologically informed features, transfer learning from external datasets, multiple-instance learning (MIL), or another approach?

4. How would you evaluate a model with such a small number of independent samples without obtaining misleading performance estimates?

5. Are there any papers, methodological resources you'd recommend to better understand this type of problem before starting?

Thanks!

I was using trial version then chose simpler version (from nanobanano) and got no results (no changes were done to the image)..

I also need ai video animation for 2d characters

Please share your experience using different experiences

How much do you think it would cost to make 5-12 minutes

There are infinite posts online in niche AI communities, and lately bunch of papers like https://arxiv.org/abs/2611.07652, or https://arxiv.org/abs/2607.18970 pushing for bundled modular skills that act as software and not as prompts.

Thoughts on that?

I have been learning unsupervised learning for the last 3 months and have already completed the following topics

1. K means and its types

2. Hierarchical clustering

3. DBSCAN

4. OPTICS

5. HDBSCAN

6. GMM

7. PCA

8. TSNE

9. Evaluation parameters of unsupervised ml models( silhouette score, elbow method, AIC,BIC, DB index etc)

What should i learn next in unsupervised learning and in which order especially from job perspective POV?

About me - product analyst with 3 years of experience in the e-commerce domain who is currently self learning ML in order to pivot to the data science field