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      Learn How to Create Features from Tabular Data and Accelerate your Data Science Pipeline

      , Sr. Data Scientist, NVIDIA
      , Manager - Deep Learning for RecSys, NVIDIA
      , Senior Data Scientist, NVIDIA
      , Senior Data Scientist, NVIDIA

      Feature engineering is an important component in (tabular) machine learning problems, which can be easily integrated into an existing model. The tabular data structure limits the models' capabilities to learn the relationships between features, and adding handcrafted features can significantly boost their performance. We'll teach best practices for feature engineering techniques specific to tabular data building off our teams' collective experience competing in data science competitions such as Kaggle and RecSys. We're going to use RAPIDS, an open-source software that accelerates the whole data science pipeline from data preprocess/engineering to machine learning on GPU. Become familiar with RAPIDS' pandas/scikit-learn-like Python API and experience the blazing speed-up it provides. 

       

      Prerequisite(s):  

       

      • Basic Python Programming Skills 

       

      Please disregard any reference to "Event Code" for access to training materials. "Event Codes" are only valid during the original live session. Explore more training options offered by the NVIDIA Deep Learning Institute (DLI). Choose from an extensive catalog of self-paced, online courses or instructor-led virtual workshops to help you develop key skills in AI, HPC, graphics & simulation, and more.

      活动: GTC Digital Spring
      日期: March 2023
      行业: 所有行业
      级别: 初级技术
      话题: Data Science
      语言: 英语
      话题: Data Science and Machine Learning
      所在地: