- • Data retrieval with SQL
- • Data manipulation and visualization with R, Python
- • Engineering tips with Git, Bash
Stanford
With my twin brother Afshine, we built easy-to-digest study guides highlighting the important points of each class that I TA-ed at Stanford.
- • Reflex-based models
- • States-based models
- • Variables-based models
- • Logic-based models
- • Supervised and Unsupervised Learning
- • Deep Learning
- • Machine Learning tips and tricks
- • Probabilities, Statistics, Linear Algebra and Calculus refreshers
- • Convolutional Neural Networks
- • Recurrent Neural Networks
- • Deep Learning tips and tricks
- • First and second-order ODEs and applications
- • Linear Algebra, Calculus and Trigonometry tools
- • Matlab tips
- • Key concepts of the course
- • Distribution tables, common questions
- • Matlab tips and course-related functionalities
Publications
- EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation
A. Amidi, S. Amidi, D. Vlachakis, V. Megalooikonomou, N. Paragios, E. Zacharaki
PeerJ, 2018
PDF ・ Code ・ Docs - Automatic single- and multi-label enzymatic function prediction by machine learning
S. Amidi, A. Amidi, D. Vlachakis, N. Paragios, E. Zacharaki
PeerJ, 2017
PDF ・ Code - A machine learning methodology for enzyme functional classification combining structural and protein sequence descriptors
A. Amidi, S. Amidi, D. Vlachakis, N. Paragios, E. Zacharaki
International Work-Conference on Bioinformatics and Biomedical Engineering, 2016
PDF ・ Code