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Ml Related Resource

1. https://github.com/trekhleb/homemade-machine-learning There are two folders homemade and notebook, homemade contains all well commented low level code and doc explaining maths. Notebook is like a more of API use.
2. https://github.com/Avik-Jain/100-Days-Of-ML-Code It contains more pictorial explanation with just API usage, worth seeing, might come in handy to revise quickly
3. https://github.com/rushter/MLAlgorithms This contains fundamental implementation of bit more advanced algorithms.

How I found all of these repo

1. https://github.com/topics/machine-learning-algorithms I went there and scrolled, you can do that too

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1. https://lwn.net/Articles/250967/
2. https://lwn.net/Articles/252125/
3. https://lwn.net/Articles/253361/
4. https://lwn.net/Articles/254445/
5. https://lwn.net/Articles/255364/
6. https://lwn.net/Articles/256433/
7. https://lwn.net/Articles/257209/
8. https://lwn.net/Articles/258154/
9. layering in html ,https://github.com/layerJS/layerJS
10. serverless postgres https://neon.tech/blog/hello-world/ , https://github.com/neondatabase/neon/tree/main/pageserver/src

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Agi 2

1. https://web.archive.org/web/20100728143021/http://sites.google.com/site/narswang/home/agi-introduction
2. https://cis.temple.edu/~pwang/AGI-Intro.html

Agi

1. https://www.kurzweilai.net/the-real-reasons-we-dont-have-agi-yet
2. https://www.reddit.com/r/agi
3. https://github.com/fairy-tale-agi-solutions/awesome-artificial-general-intelligence/
4. http://www.scholarpedia.org/article/Artificial_General_Intelligence
5. https://www.reddit.com/r/agi/comments/e1d9tg/have_there_been_any_architectures_to/

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1. https://medium.com/mindorks/android-design-shimmer-effect-fa7f74c68a93
2. https://medium.com/default-to-open/smooth-loading-animations-in-android-11dcae4ecfd0
3. https://github.com/thanhtoan1196/awesome-android-tips
4. https://nisrulz.com/android-tips-tricks/
5. https://github.com/runo280/awesome-android

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Blogs

1. https://blog.yugabyte.com/how-does-consensus-based-replication-work-in-distributed-databases/
2. https://news.ycombinator.com/item?id=19343698
3. https://severalnines.com/resources/database-management-tutorials/galera-cluster-mysql-tutorial
4. https://news.ycombinator.com/item?id=19343698
5. https://www.percona.com/live/plam16/sites/default/files/slides/Patroni%20PLAM_0.pdf

Implementation

Etcd 2.3

1. https://github.com/etcd-io/etcd/tree/release-2.3

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Problem

1. Hackernews app export wasn't working for all my saved article.
2. So I panicked

Solution

1. adb shell 'pm list packages -f' | grep mater
2. adb backup -f ~/backup.ab -noapk io.github.hidroh.materialistic
3. ( printf "\x1f\x8b\x08\x00\x00\x00\x00\x00" ; tail -c +25 backup.ab ) | tar xfvz -
4. Go to ~/apps/io.github.hidroh.materialistic/db
5. sqlite3 Materialistic.db

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movie analysis

1. cinemasins
2. More Than Meets the Lens
3. Every Frame a PAinting
4. Rob Ager's videos
5. David Bordwell's blog
6. Rocket Jump Film School
7. kaptainkritian
8. David chen
9. Channel Chriswell

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installation help

1. get nvidia drivers from software updates ubuntu. nvidia-440
2. get local .deb file of cuda.
3. install according to instruction
4. export PATH=/usr/local/cuda-10.2/bin${PATH:+:${PATH}}
5. export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
6. Put these commands in bashrc
7. source it.
8. download cudnn tarball
9. extract and go into that directory. you will find a directroy called "cuda".

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Practical Resource

1. https://edwardbetts.com/monograph/Sharpe_ratio
2. https://medium.com/engineer-quant
3. https://news.ycombinator.com/item?id=21610687
4. https://silburt.github.io/blog/fundamental_analysis.html
5. https://towardsdatascience.com/stock-analysis-in-python-a0054e2c1a4c
6. https://github.com/grananqvist/Awesome-Quant-Machine-Learning-Trading
7. https://github.com/VivekPa/OptimalPortfolio
8. https://github.com/faizanahemad/data-science-utils/tree/master/data_science_utils/financial
9. https://github.com/PyDataBlog/Python-for-Data-Science