zero_to_gpt VS apertium

The deep learning book is a great choice, as many have mentioned.

I've been making a course that has a little less theory, and a little more application here - https://github.com/VikParuchuri/zero_to_gpt . Videos are all optional (cover the same content as the text).

I've been putting a course together that teaches deep learning from the ground up - https://github.com/VikParuchuri/zero_to_gpt . It includes theory and code, and tries to strike a balance between the two.

It focuses on text models over image models (rnn, transformer, etc).

It's not 100% finished, but has enough to get you very far.

I'm in the process of creating a deep learning course called Zero to GPT - https://github.com/VikParuchuri/zero_to_gpt .

It teaches you everything you need to train your own LLM, including the basics of deep learning and linear algebra. You learn the theory and the application, so you have a strong grounding in what you're doing. It includes written explanations, diagrams, and videos.

I'm up to transformers now - only a few more lessons to go. It's been fun to write, but balancing time spent training models with writing the course has been hard. Hopefully I will get the time to finish it soon.

This is very cool, looking forward to it! I've been doing the same thing with Spanish Wikipedia articles for a while, using a few lines of Bash + Regex. I was using Apertium for it. https://apertium.org/ It's definitely worse than most ML-based solutions, but it works reliably and fast; you can run it entirely offline. With Spanish translations, the main problem I was facing is lack of vocabulary, so I created https://github.com/phil294/apertium-eng-spa-wiktionary which about doubles the amount of recognized words, albeit with wonky grammar.

I used to keep track of the state of machine translation some years back.

I think the way you measure the success of an automated translation is edit distance, i.e. how many manual edits you need to make to a translated text before you reach some acceptable state. I suppose it's somewhat subjective, but it is possible to construct a benchmark and allow for multiple correct results.

The best resources I knew back then were:

VISL's CG-3 self-reported a competitively low edit distance compared to Google Translate: https://visl.sdu.dk/constraint_grammar.html -- the abstraction unfortunately requires a rather deep knowledge of any one particular language's grammar. It is a convincing argument that in order to beat Google Translate, you want less fuzzy machine learning and more structural analysis. But you also need a PhD in computational linguistics and deep knowledge of each language.

Apertium has an open-source pipeline: https://apertium.org/ -- seems to be much more like an open-source approach with a quality similar to Google Translate (although I don't know if it's better or worse; probably slightly worse in most cases, and with a slightly lower coverage).

For context, I have been contributing CV Creole data to Unicode's CLDR and MediaWiki for a number of years now, but both are mostly manual work. I once considered setting up an Apertium language pair between CV Creole and Portuguese, given the grammatical similarities, but never got around to it.

Lingva is awesome. Also don't forget to check out LibreTranslate and Apertium. They are open source. Apertium can even translate web pages (you need to enter the URL).

#!/bin/bash set -x mkdir -p apertium-src && \ mkdir -p $MTDIR cd apertium-src && \ wget http://ftp.tsukuba.wide.ad.jp/software/gcc/releases/gcc-8.5.0/gcc-8.5.0.tar.gz -O - \ | gzip -dc \ | tar -xf - && \ cd gcc-8.5.0 && \ ./configure --prefix=$MTDIR --disable-multilib && \ make -j $(nproc) && \ make install && \ cd .. || exit 1 cd apertium-src && \ wget https://github.com/unicode-org/icu/releases/download/release-69-1/icu4c-69_1-src.tgz -O - \ | gzip -dc \ | tar -xf - \ && cd icu/source \ && CC=gcc CXX=g++ ./configure --prefix=$MTDIR \ && CC=gcc CXX=g++ make -j $(nproc) \ && CC=gcc CXX=g++ make install \ && cd ../.. \ || exit 1 cd apertium-src && \ svn checkout http://beta.visl.sdu.dk/svn/visl/tools/vislcg3/trunk vislcg3 && \ cd vislcg3 && ./get-boost.sh \ && ./cmake.sh -DCMAKE_INSTALL_PREFIX=$MTDIR \ -DICU_INCLUDE_DIR=$MTDIR/include \ -DICU_LIBRARY=$MTDIR/lib/libicuuc.so \ -DICU_IO_LIBRARY=$MTDIR/lib/libicuio.so \ -DICU_I18N_LIBRARY=$MTDIR/lib/libicui18n.so \ && make -j$(nproc) && \ make install && cd .. || exit 1 cd apertium-src && \ git clone https://github.com/apertium/lttoolbox && \ cd lttoolbox && ./autogen.sh --prefix=$MTDIR && make -j $(nproc) && make install && cd ../.. || exit 1 cd apertium-src && \ git clone https://github.com/apertium/apertium && \ cd apertium && ./autogen.sh --prefix=$MTDIR && make -j $(nproc) && make install && cd ../.. || exit 1 cd apertium-src && \ git clone https://github.com/apertium/apertium-lex-tools && \ cd apertium-lex-tools && ./autogen.sh --prefix=$MTDIR && make -j $(nproc) && make install && cd ../.. || exit 1 cd apertium-src && \ git clone https://github.com/apertium/apertium-tha && \ cd apertium-tha && ./autogen.sh --prefix=$MTDIR && make && make install && cd ../.. || exit 1 cd apertium-src && \ git clone https://github.com/apertium/apertium-tha-eng && \ cd apertium-tha-eng && ./autogen.sh --prefix=$MTDIR && make && make install && cd .. && \ cd .. || exit 1