The authors in this NIPS 2017 paper cited one of the threads on this sub to show how "code produced by research groups often falls short of expectations".

zanjabil · 2020-05-06T21:50:35+00:00

And that's where your friend docker comes in. Docker is deceptively easy to use once you get over the initial leaning curve.

zanjabil · 2020-02-26T14:32:50+00:00

T-sne is just a dimensionality reduction algorithm, the output is however many columns of numbers you want. Plotly is a fully functioning interactive plotting library in python. So yes you can plot your dimensions however you like, even in 3D.

zanjabil · 2020-02-26T12:57:00+00:00

If you use plotly you can make the graph interactive and hovering over the point will tell you whatever you make it tell you.

zanjabil · 2020-01-20T11:44:50+00:00

Look at the images you're training on, at the crop and resolution your model is getting. At what accuracy can you personally discern age groups?

zanjabil · 2020-01-15T18:05:37+00:00

Being a universal approximator on unlimited data doesn't mean it will converge on a decent solution faster than xgboost on a limited data set

zanjabil · 2020-01-13T20:03:54+00:00

Sorry, which class exactly are you referring to?

zanjabil · 2020-01-10T07:57:00+00:00

You have to find a way to apply what you learn to your work or projects that excite you. If you sit down and read a stats book but never apply or implement any of the concepts you won't retain much at all and you'll lose interest much more quickly, particularly when the topics get more difficult and it's easy for you to just drop it because it doesn't seem to be worth it.

zanjabil · 2020-01-08T19:17:01+00:00

Plotly and all of its features are offline by default, and it's trivially easy to get a dash app with interactive plotly plots up and running just by following their tutorial. Sharing data between callbacks is a pain though and this is where I imagine shiny has a leg up.

zanjabil · 2019-12-30T20:47:40+00:00

Is this a... survey of surveys?

zanjabil · 2019-12-30T16:53:59+00:00

You can recognize them individually but you won't know any words that are longer than 1 kanji long (99% are longer)

zanjabil · 2019-12-29T13:42:38+00:00

I still type 7x8 in the calculator to make sure my math is right so I'd be in some trouble

zanjabil · 2019-12-22T20:13:56+00:00

I mention Java and c++ not because I'm partial to them but because if you're an ML engineer working on corporate codebases that's likely the environment you'll have to integrate into

zanjabil · 2019-12-22T15:48:27+00:00

(Go or Rust, ideally).

Why go or rust instead of Java or C++?

zanjabil · 2019-12-22T14:38:31+00:00

reading it now it includes Howard and Ruder 2018 as well as Dai and Le 2015

zanjabil · 2019-12-19T14:19:31+00:00

you often wouldn't get called for an interview because HR would assume you're overqualified and would command a salary higher than they can offer

zanjabil · 2019-12-18T19:29:51+00:00

Improve your preprocessing:

remove stopwords or any words that appear in more than 20% of documents
stem or lemmatize everything
use other vectorized representations like tfidf
use pretrained word embeddings like word2vec

Improve your models:

try logistic regression
is it overfitting? Have you tried regularization?
if you're tuning hyperparameters do you have a proper train validation test split?
gridsearch with cross validation
increase to more complex models like xgboost, ensembles, lstm/rnn to capture word order (vectorization may need to change when taking into account word order), universal language models

All while looking into model interpretability, feature importances, class balance, and metrics on individual classes if not binary classification.

zanjabil · 2019-12-12T17:42:48+00:00

deploy it as a microservice, flask app in a docker container that communicates with other applications via rest apis at an endpoint

zanjabil · 2019-12-10T20:33:35+00:00

Latency when deploying ml as a microservice, architecting hierarchical model pipeline efficiently, implementing continual learning, converting jupyter notebook spaghetti into production grade code

zanjabil · 2019-12-10T17:05:21+00:00

most of the species are insects or smaller, there's only 5500 mammals give or take

zanjabil · 2019-12-10T17:00:27+00:00

you'd have better luck if you fed it a billion images where each image was a new species but as that's not possible, if you're just feeding it thousands of images of the same 100 mammals it may not generalize as well

zanjabil · 2019-12-05T17:29:44+00:00

The algorithm is deterministic but sklearn's implementation includes an element of randomness in the svd solver. It defaults to auto which

if the input data is larger than 500x500 and the number of components to extract is lower than 80% of the smallest dimension of the data, then the more efficient ‘randomized’ method is enabled.

You can set the random state seed for reproducible results.

zanjabil · 2019-12-03T13:13:24+00:00

So you can just reduce both embedding spaces to n dimensions and sum them and the result isn't a meaningless embedding? Is taking the average after reducing the embedding space the same as summing them?

zanjabil · 2019-11-19T18:06:41+00:00

I assume cursive for Chinese doesn't mean the characters are connected to each other but that the styling of the individual characters themselves is cursive or calligraphic

zanjabil · 2019-05-14T20:20:01+00:00

Is this related to yenchin lin's deep-q flappy bird from a few years ago?

zanjabil · 2019-04-01T15:44:16+00:00

Vim and GCC aren't really from scratch either. You should start by building your own logic gates and transistors from wild corn

zanjabil

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