My name is Hair Parra, Data Engineer & Data Scientist at Cisco, and B.A. C.S., Statistics, and Linguistics graduate at McGill University. I'm a machine learning, statistics, NLP, and data science enthusiast, a technical article writer, and a lifelong learner. This blog-portfolio contains various of my articles and achievements, as well as links to my other professional sites. Feel free to contact me by LinkedIn for any industry or academic purposes!
Having completed the third course of Deeplearning.ai's Deep learning specialization: Structuring Machine Learning Projects Certification, I was able to gain insight on many important concepts to which not much attention is often given, but are nonetheless crucial in implementing effective machine learning systems, such as orthogonalization, metrics selection appropriate to the problem, satisficing vs. optimizing metrics, the correct split of training/dev/test sets, analysis of error including avoidable bias, training/dev/set errors, the notion of Bayesian optimal error and "super-human" performance, ways to analyze and deal with bias, variance, and data mismatch problems, and important paradigms such as multi-task learning, transfer learning, end-to-end learning.