Alexander Korbonits

Python is the glue that makes my day-to-day work as a data scientist sing. I use Python primarily for data mining and data science, but also for data engineering tasks, especially ETL.

I have used Apache Spark extensively for data science projects, particularly making use of MLLib for doing supervised and unsupervised learning on large, sparse data formats stored in HDFS. I tend to use both the Scala and Python APIs, depending on how fast I want to iterate and also where the current functionality is exposed. The new dataframe abstraction is great, and works well with existing Python libraries when using the PySpark API, such as Pandas and Graphlab-Create.

This summer I have given three public talks about doing Deep Learning using Python. I am familiar with several Python-related deep learning interfaces, including, but not limited to, caffe, Theano, Torch, Graphlab-Create, Nervana Sys, etc. Additionally, I am studying the theoretical properties of different kinds of neural networks and am applying this knowledge to analyze interesting data sets and solve interesting data problems, such as computer vision and transfer learning. Slides from one of my talks: http://www.slideshare.net/AlexanderKorbonits/deep-learning-with-python-pydata-seattle-2015 Video from one of my talks: https://www.youtube.com/watch?v=MVyauNNinC0