- Distributed and Scalable Machine Learning [Webinar] - Feb 17, 2021.
Mike McCarty and Gil Forsyth work at the Capital One Center for Machine Learning, where they are building internal PyData libraries that scale with Dask and RAPIDS. For this webinar, Feb 23 @ 2 pm PST, 5pm EST, they’ll join Hugo Bowne-Anderson and Matthew Rocklin to discuss their journey to scale data science and machine learning in Python.
Capital One, Dask, Distributed, Machine Learning, Python, scikit-learn, XGBoost
- KDnuggets™ News 20:n13, Apr 1: Effective visualizations for pandemic storytelling; Machine learning for time series forecasting - Apr 1, 2020.
This week, read about the power of effective visualizations for pandemic storytelling; see how (not) to use machine learning for time series forecasting; learn about a deep learning breakthrough: a sub-linear deep learning algorithm that does not need a GPU?; familiarize yourself with how to painlessly analyze your time series; check out what can we learn from the latest coronavirus trends; and... KDnuggets topics?!? Also, much more.
Coronavirus, Data Visualization, Deep Learning, Distributed, Machine Learning, Python, Time Series
- 3 Key Trends in the DBMS Market - May 3, 2014.
The top 3 trends in DBMS include market consolidation, moving beyond OLTP, and distributed computing - we examine them in detail.
DBMS, Distributed, Gartner, Michael Waclawiczek, NuoDB, OLTP, SQL, Trends
- DEAP, Distributed Evolutionary Algorithms in Python, Framework for Rapid Prototyping - Feb 20, 2014.
DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas, seeking to make algorithms explicit and data structures transparent. Free Download.
DEAP, Distributed, Evolutionary Algorithm, Python, Rapid Prototyping
