Efthimis Vaiopoulos

@ Physisist, MSc Inf. Systems, ML/DL Specialist, AI Catalyst Member

Specialties (25 years of experience):
– ML/DL/AI engineering with a strong focus in Computer Vision.
– Building intelligent software components to provide solutions that improve processes/products.
– AI software/application integration in ICT systems (from problem understanding to model training to final deployment and production).
– Data Science & Business Intelligence (traditional DWH/ETL/BI infrastructure design).
– Information Systems Engineering (Analysis, Design, Integration, Testing, Deployment).
– Data/Process Engineering & Business Analysis.
– ICT Project Management.

ML/DL applications:
– Computer Vision applications like faults optical detection (with tensorflow and other DL frameworks).
– Pattern discovery in health-care (custom Python implementation using numpy & other packages).
– Systems logging analysis (with SciKitLearn and Python).
– CRM & logistics data-mining applications (SPSS & R implementations).
– Web tools for applying Statistics & ML on custom datasets (in R).

ML/DL sources (indicative list):
– Deep Learning & Machine Learning books, like the Hands on ML book by Geron, 2019, 2022.
– Deep Learning tutorials & notebooks (from Kaggle and several other blogs), scientific papers & MOOCs.
– Tensorflow, Pytorch and other DL frameworks tutorials & notebooks.
– Data Science and Data Analysis books, tutorials & notebooks.
– Python packages documentation for DL, ML and DS.
Older sources (indicative list):
– Boosting: Foundations and Algorithms (Schapire, Freund, MIT Press, 2014).
– An Introduction to Statistical Learning with R (Hastie et al, Springer, 2013).
– R & R Studio tutorials and documentation.
– Applied Predictive Modeling (Kuhn, Johnson, Springer, 2013) .
– The Elements of Statistical Learning (Hastie et al, Springer, 2009).
– Pattern Recognition and Machine Learning (Bishop, Springer, 2007).
– Introduction to Data Mining (Tan et al, Pearson, 2005).
– Data Mining, Practical Machine Learning Tools and Techniques (Whitten, Kaufmann, 2004).
– Mastering Data Mining (Berry & Linoff, Wiley, 1999).My job as a ML/DL Engineer is to support companies and Organizations with the appropriate tools and knowledge that will allow their teams build intelligent software components and integrate them into current ICT infrastructure. Specialties (25 years of experience): – ML/DL/AI engineering with a strong focus in Computer Vision. – Building intelligent software components to provide solutions that improve processes/products. – AI software/application integration in ICT systems (from problem understanding to model training to final deployment and production). – Data Science & Business Intelligence (traditional DWH/ETL/BI infrastructure design). – Information Systems Engineering (Analysis, Design, Integration, Testing, Deployment). – Data/Process Engineering & Business Analysis. – ICT Project Management. ML/DL applications: – Computer Vision applications like faults optical detection (with tensorflow and other DL frameworks). – Pattern discovery in health-care (custom Python implementation using numpy & other packages). – Systems logging analysis (with SciKitLearn and Python). – CRM & logistics data-mining applications (SPSS & R implementations). – Web tools for applying Statistics & ML on custom datasets (in R). ML/DL sources (indicative list): – Deep Learning & Machine Learning books, like the Hands on ML book by Geron, 2019, 2022. – Deep Learning tutorials & notebooks (from Kaggle and several other blogs), scientific papers & MOOCs. – Tensorflow, Pytorch and other DL frameworks tutorials & notebooks. – Data Science and Data Analysis books, tutorials & notebooks. – Python packages documentation for DL, ML and DS. Older sources (indicative list): – Boosting: Foundations and Algorithms (Schapire, Freund, MIT Press, 2014). – An Introduction to Statistical Learning with R (Hastie et al, Springer, 2013). – R & R Studio tutorials and documentation. – Applied Predictive Modeling (Kuhn, Johnson, Springer, 2013) . – The Elements of Statistical Learning (Hastie et al, Springer, 2009). – Pattern Recognition and Machine Learning (Bishop, Springer, 2007). – Introduction to Data Mining (Tan et al, Pearson, 2005). – Data Mining, Practical Machine Learning Tools and Techniques (Whitten, Kaufmann, 2004). – Mastering Data Mining (Berry & Linoff, Wiley, 1999).