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Quality data science outputs depend on quality inputs. Data cleansing and preparing may not be exciting work, but it’s critical.
Triton uses Python’s syntax to compile to GPU-native code, without the complexities of GPU programming.
See how easy it is to create interactive web graphs from ggplot2 visualizations with the ggiraph R package. You can even link graphs so that clicking one dataviz affects the display of another.
Aligning the right metrics to the right use case allows for timelier reporting and reduces application risk.
Time series forecasts are used to predict a future value or a classification at a particular point in time. Here’s a brief overview of their common uses and how they are developed.
SciPy, Numba, Cython, Dask, Vaex, and Intel SDC all have new versions that aid big data analytics and machine learning projects.
The next frontier for data processing is a new platform capable of delivering insights, actions, and value the instant data is born.
Deep learning is solving challenging problems in industries as diverse as retail, manufacturing, and agriculture. These companies are leading the way.
Despite the hype, especially around self-driving cars, AI is writing code, designing Google chip floor plans, and telling us how much to trust it.
See how to use R to query data in Google BigQuery with the bigrquery and dplyr R packages.