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 :/