Special Issue "Machine Learning with Python"
Deadline for manuscript submissions: closed (20 December 2019).
Interests: machine learning; deep learning; statistics; computational biology
We live in this day and age where quintillions of bytes of data are generated and collected every day. Around the globe, researchers and companies are leveraging these vast amounts of data in countless application areas, ranging from drug discovery to improving transportation with self-driving cars.
As we all know, Python evolved into the lingua franca of machine learning and artificial intelligence research over the last couple of years. What makes Python particularly attractive for us researchers is that it gives us access to a cohesive set of tools for scientific computing and is easy to teach and learn. Also, as a language that bridges many different technologies and different fields, Python fosters interdisciplinary collaboration. And besides making us more productive in our research, sharing tools we develop in Python has the potential to reach a wide audience and benefit the broader research community.
Fortunately, we have seen a surge in introductory and original teaching material that has been written about machine learning with Python in the last couple of years. This body of tutorials and case studies is enabling both young and interdisciplinary researchers to leverage the rich toolsets for machine learning research and data science applications available in Python. While most literature focuses on introductory topics, however, the focus of this special issue is on the implementation of new algorithms and methods implemented in Python and essential applications in the fields of data science, machine learning, and deep learning.
This Special Issue aims to collect a body of advanced literature written by experts that provides access to the state-of-the-art methodology developed with Python. The mission is to advance the existing body of literature by sharing contemporary, cutting edge research enabled by Python. Moreover, we aim to provide representative applications of new, state-of-the-art libraries that facilitate modern problem-solving as valuable resources for researchers to utilize machine learning at the leading edge.
Dr. Sebastian Raschka
- data processing pipelines
- distributed training
- reproducible data science
- dimensionality reduction and feature selection
- deep learning