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https://github.com/vpereira01/gostart

Playground of Go, ml and prime factorization
https://github.com/vpereira01/gostart

factorization golang jupyter-notebook machine-learning primes

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Playground of Go, ml and prime factorization

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# What is this?

My playground to learn Go, machine learning and prime factorization.

# Outcomes

* Learned some Go
* Go routines, channels and waitGroups makes it easy to implement multi-threaded workloads
* Performance tuning with `go test -cpuprofile cpu.prof` and `go tool pprof -http localhost:19123 cpu.prof` is also very easy
* Learned some Machine Learning
* Focused on scikit-learn which seems the most easier tool to start on
* Normalization of data with scaling to make raw data processable by ordinary python tools
* That's always good to start by evaluating features/labels correlation with 'seaborn.pairplot'
* Jupyter notebooks are very useful for quick iteration
* Learned about semi-primes factorization
* Initially approach to use machine learning to estimate prime factors sum is not useful:
* Although neural-network prediction score can be high, greater than 0.99, after de-normalization the actual error is very high
* Trying to find the real prime factors sum based on neural-network prediction is way slower than using other methods
* About the Fermat's factorization method
* Tried a different approach, using only algorithms, to find the prime factor difference with a good performance
* Afterwards re-checked Wikipedia and found that I just implemented Fermat's factorization :/