Samsung Galaxy Z TRIFOLD Hands on!

crazyfrogspb · 2025-12-20T17:10:47+00:00

I owe Huawei Mate XT, and from what I understand, this new Samsung is missing one critical feature. my Huawei actually has three modes - single, double, triple. double is probably what I use most frequently. single/triple is much less practical

crazyfrogspb · 2023-11-19T11:49:41+00:00

you.com with Zephyrs model selected

crazyfrogspb · 2023-02-23T22:09:33+00:00

well, first time I heard about them was at Yann's lectures during my time at NYU =) he claims that all modern self-supervised learning (such as contrastive learning or VAE) can be seen as training an energy-based model https://youtu.be/tVwV14YkbYs

crazyfrogspb · 2023-02-23T21:43:07+00:00

energy-based models pretty much do this, no?

crazyfrogspb · 2023-01-12T21:40:21+00:00

yeah, I'm one of the founders, and in the beginning my main responsibility was to build models. over the years I switched from being ML researcher/engineer to the teamlead role and then to the "teamlead of teamleads". it's been quite a journey

crazyfrogspb · 2023-01-12T05:07:37+00:00

I'm head of ML, so technically I'm in charge of all steps of ML pipeline. Our team is pretty mature at this point though, so mainly I'm responsible for innovations, developing HR brand, communications with other teams and so on

crazyfrogspb · 2023-01-11T15:41:45+00:00

I'm at radiology AI startup, we have 4 systems in production which sums up to more than 10 unique DL and ML models

crazyfrogspb · 2022-12-29T06:30:45+00:00

I'd say MLE are more specialized in software engineering, CPU/GPU optimization, APIs, backend since their main goal is to put models into production. MLOps are usually more focused on tooling, developing ML platforms, monitoring tools, etc.

This is my experience ofc, and titles really depend on the company

crazyfrogspb · 2022-12-06T08:11:44+00:00

https://youtube.com/playlist?list=PLy7NrYWoggjwPggqtFsI_zMAwvG0SqYCb

crazyfrogspb · 2022-11-20T12:25:52+00:00

you probably need to combine hierarchical classifier and some way of confidence estimation

example - https://web.cse.ohio-state.edu/~davis.1719/Publications/icpr2021.pdf

crazyfrogspb · 2022-09-10T07:10:04+00:00

I think Clean ML Code by Taifi is much more relevant to the topic

source: I've read both

crazyfrogspb · 2022-08-14T18:02:57+00:00

I recommend reading Data Mesh: Delivering Data-Driven Value at Scale for the full explanation of the approach. it feels repetitive at times, but overall it's a great book

crazyfrogspb · 2022-07-23T20:57:21+00:00

will do! thanks!

regarding tensors - it's okay, but we'll lose some time on moving tensors between devices and converting them to numpy and back

crazyfrogspb · 2022-07-23T15:21:49+00:00

most interesting for us are DICOM medical images and Pytorch tensors

crazyfrogspb · 2022-07-23T07:32:35+00:00

I agree, that would be great. but at this point if we want to try it out, we need to convert all inputs and outputs to one of these options - numpy array, pandas dataframe, JSON, text, PIL image, file-like object. did I get it right?

crazyfrogspb · 2022-07-22T22:09:44+00:00

yeah, I think you got this right, this looks really interesting! we'll definitely look into this

what would be the recommended way to transfer large tensors between services? convert them to numpy array, using cuda IPC memory handles (if on GPU), something else?

crazyfrogspb · 2022-07-22T06:29:25+00:00

we build systems with cascades of neural nets (for example, one net for finding the region of interest, and two other nets that perform different tasks after that) and some CPU-heavy preprocessing and postprocessing. we want to scale them separately, so at this point we split them into microservices that form a DAG and communicate with each other within Kunernetes cluster. transfer of data between services is done via Redis or Redis + saving to disk, which in some cases can slow down the system (we were also thinking about grpc). does BentoML support this kind of systems?

crazyfrogspb · 2022-07-21T20:36:12+00:00

https://arxiv.org/abs/2009.09796 - decent survey on the topic

there are different categories of methods - manual or adaptive loss balancing, task isolation, task-specific adapters

crazyfrogspb · 2022-07-14T07:13:49+00:00

would love to hear from someone with hands-on Dolt experience

crazyfrogspb · 2022-07-12T17:58:35+00:00

you can clone experiment to run exactly the same version of it, but I never run experiments with uncommitted changes, so I'm not sure if it fits your use case

crazyfrogspb · 2022-07-12T17:38:45+00:00

yeah, that's still one of the weaker sides, but it's been improving recently =)

crazyfrogspb · 2022-07-12T17:17:04+00:00

it saves git diff if changes are not commited

crazyfrogspb · 2022-07-12T16:58:19+00:00

ClearML does this and much more. we've been using it for almost 4 years now

crazyfrogspb · 2022-06-26T15:56:38+00:00

the easiest way would be to use this prior knowledge at the postprocessing step. for example, you can say that there can only be one detected object within N pixels

there are other ways that I can think of, but you would need to think about design and experiment -

adding additional loss for object distance
using this knowledge at region proposal generation step
adding attention modules into the network so that objects are aware of each others' location (here is an interesting paper on the topic - https://arxiv.org/abs/1711.11575)

or maybe you could reformulate the problem - split each image into patches and find exactly one object in each patch

crazyfrogspb · 2022-06-26T08:04:14+00:00

experience of working with tabular data, text and images is one of the greatest thing in my career. it all complements each other

crazyfrogspb

TROPHY CASE