Sign language visual recognition from continuous multi-modal streams is still one of the most challenging fields. Recent advances in human actions recognition are exploiting the ascension of GPU-based learning from massive data, and are getting closer to human-like performances. They are then prone to creating interactive services for the deaf and hearing-impaired communities. A population that is expected to grow considerably in the years to come. This paper aims at reviewing the human actions recognition literature with the sign-language visual understanding as a scope. The methods analyzed will be mainly organized according to the different types of unimodal inputs exploited, their relative multi-modal combinations and pipeline steps. In each section, we will detail and compare the related datasets, approaches then distinguish the still open contribution paths suitable for the creation of sign language related services. Special attention will be paid to the approaches and commercial solutions handling facial expressions and continuous signing.

As a Data Scientist, I use pandas daily and I am always amazed by how many functionalities it has. These 5 pandas tricks will make you better with Exploratory Data Analysis, which is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. Many complex visualizations can be achieved with pandas and usually, there is no need to import other libraries. To run the examples download this Jupyter notebook. In case you've missed 5 lesser-known pandas tricks.

Editor's Note: See Boris Kovalerchuk's talk "Interpretable Knowledge Discovery Reinforced by Visual Methods" at ODSC West 2019. Visual reasoning and discovery have a long history. Chinese and Indians had visual proof of the Pythagorean Theorem in 600 B.C. before it was known to the Greeks. Scientists such as Bohr, Boltzmann, Einstein, Faraday, Feynman, Heisenberg, Helmholtz, Herschel, Kekule, Maxwell, Poincare, Tesla, Watson, and Watt have declared the fundamental role that images played in their most creative thinking. The fundamental challenge for visual creative thinking and discovering in multidimensional data (n-D data) used in machine learning (ML) is that we cannot see multidimensional data with a naked eye.