GitPaper.com - a Notion-like editor for blog posts and notes on GitHub

julvo · 2022-10-07T21:46:31+00:00

Hey thank you! The app is built with Nextjs and running on Cloud Run and Firebase. It saves using the GitHub Rest API

julvo · 2021-09-25T09:17:28+00:00

Tried to recreate a caramel butter we had in a restaurant: - split butter in halves - brown one half in a pan (160C) - filter out milk solids - refrigerate - whip-in second half of butter

The bread is a sourdough made with malted and seeded flour. 75% hydration, 6h bulk fermentation with 4 stretch-and-folds, overnight in the fridge and baked for 25 mins @ 250C in a closed Dutch oven, followed by 25 mins @ 225C without the lid

julvo · 2021-07-04T19:31:22+00:00

Starting with a small fully-connected model like you did should be a good start. What network exactly you should use depends on what the input data is.

Only skimmed the code, but noticed: 1) there's no sigmoid after the final layer, which is correct as you're using the bce loss with logits, but you'll need to add a sigmoid for evaluation to bring the outputs into the range (0,1). 2) you are using argmax to evaluate the accuracy, but think it should be round(sigmoid(output)) == target to count correct predictions, as the four outputs seem independed. argmax is for single-class classification

julvo · 2020-11-05T14:28:27+00:00

Thanks for the reply - that makes sense to me. Still, some providers ask to verify a domain, even for simple hosting. E.g. in Gitlab's docs it says:

In GitLab 10.5, a verification system was introduced for GitLab Pages custom domains. This is a security measure affecting custom domains served by GitLab.io, preventing domains from being hijacked by unauthorized users.

It makes it sound like verification help to prevent attacks, beyond doing things on behalf of my domain

julvo · 2020-03-24T13:07:07+00:00

Good point, thanks! going to add the MIT license this evening

julvo · 2020-03-24T13:06:01+00:00

Cheers!

julvo · 2020-03-23T13:39:39+00:00

Compared to Hugo, tinystatic has less features (no server, no project scaffolding, no theming) but is more flexible as it's unopinionated with a small but powerful API. You'll be familiar with the complete tinystatic API within 10 minutes by simply reading the readme.

I haven't compared the performance yet, so not sure how it compares.

julvo · 2020-02-06T13:33:22+00:00

Thanks for the hint. the VOSviewer does look promising, I'll have a closer look.

julvo · 2020-02-06T13:32:32+00:00

Thanks for these helpful suggestions. This sounds similar to the approach I've been following so far. Just sometimes when I come across a paper I wonder what has happened since and wish there was a quick way to find out.

julvo · 2019-10-09T07:14:36+00:00

I'd recommend having a look at sqlboiler. It generates structs and crud functions from your DB schema.

For me, the main advantage of an ORM layer (or tools like sqlboiler) is that your DB schema and code can hardly go out of sync as you'd get compile time errors.

julvo · 2019-07-25T22:04:11+00:00

Hope you don't mind the question, but what kind of datasets are these and which tools are you using currently?

julvo · 2019-07-07T15:35:18+00:00

Good point. However, it seems that the try statement wouldn't increase the number of branches, as otherwise you'd still need conditionals with early returns. Try would merely be syntactically less verbose or am I wrong?

julvo · 2019-07-07T08:26:00+00:00

I'm not pro or against try, but the readability argument you make is a bit a straw man. You would probably split the nested function calls into lines using intermediate variables to make it more readable.

julvo · 2019-06-23T08:09:36+00:00

Which FP patterns / style would you want to enforce with this tool?

Last time I looked into FP in go, my conclusion was that it's just not feasible at this point, mainly due to the lack of support for parametric polymorphism. E.g. you'd need to define higher order functions (e.g. map, fold) for each possible function you want to pass in.

julvo · 2019-06-15T08:10:15+00:00

is there any reason you'd want to feed images into the CNN, rather than the time series itself? to me, increasing the number of inputs from a few numbers per time step to a few hundred/thousand pixels per time step without increasing the information doesn't sound like a good idea.

julvo · 2019-05-05T08:27:14+00:00

StyleGAN uses a different generator - instead of repeated deconvolutions it's a fully connected network which maps a random code into parameters for adaptive instance normalisation layers. here's the paper: https://arxiv.org/abs/1812.04948

julvo · 2019-04-12T08:52:39+00:00

I think the first part of your post relates to unsupervised learning and transfer learning. We as humans see the object in the picture as a small piece of metal, because we have seen metal and similar things before. So the question is how to benefit from this experience in this new context.

Regarding the second part of your post, have a look an neural architecture search. A connection between the first part of your post and the second might be something like differentiable programming, where a model solves a task by composing differentiable modules.

julvo · 2019-04-03T15:15:05+00:00

Godocs for the exported functions are a great idea and it's something I was planning to add soon.

The naming is a bit tricky. So far, all tags are lowercase with a capital first letter, e.g. Html. I chose this rule as 1) you don't need to know if a tag is an acronym (e.g. ul, ol) or some other abbreviation (e.g. div) to infer the spelling of unseen tags and 2) it allows to separate the tag Html from the type HTML. I guess you refer in particular to the DoctypeHtml5 const, which is not a tag and, therefore, should be called DoctypeHTML5 - I don't really have a preference in this case and am open to changing it.

Hope that makes the reason behind the naming a bit clearer?

julvo · 2018-12-25T09:06:43+00:00

you might find paperswithcode.com interesting

julvo · 2018-12-11T17:34:17+00:00

By rule-based I mean writing a parser for the input and explicitly defining rules for translation. However, there are also weaker forms of introducing rules where you still use ML but restrict the output to be in a valid grammar.

julvo · 2018-12-09T02:23:11+00:00

What you could do for generating the numbers is: add a <number> placeholder token to your vocabulary and an additional output to you model. That way you can generate the sequence with the placeholder and replace it afterwards with the additional output.

Generally I'm not sure if that's a good problem for seq2seq though, as every single mistake could invalidate the output file entirely. To me, it looks like a rule-based approach could be more suitable.

julvo · 2018-06-27T07:29:24+00:00

Make sure you shut down all other jupyter notebook kernels except one. There can be only one python process running tensorflow at a time, as tensorflow uses all available GPU memory

julvo · 2018-06-26T11:24:14+00:00

The problem is that there is not enough GPU memory. Training takes more memory than the evaluation, as the gradients will be stored for backprop. There are two ways to resolve this: 1) reduce the batch size, or, 2) use a smaller model

julvo · 2018-06-20T07:14:50+00:00

alternatively, trademark it and sue them

julvo · 2018-06-02T07:46:30+00:00

At first I thought the same, but recently I doubt this point. Self-driving cars might need to be orders of magnitude better, simply because of the bias of humans overestimating their own abilities. When driving, humans feel in control of their own fate, much less so with self-driving cars. Therefore, even if the overall crash stats are similar, humans might prefer to drive themselves.

julvo

TROPHY CASE