Applications of AI for Predictive Maintenance (AAPM) – Perfil

Esquema Detallado del Curso

Introduction

  • Meet the instructor.
  • Create an account at courses.nvidia.com/join

Training XGBoost Models with RAPIDS for Time Series

  • Learn how to predict part failures using XGBoost classification on GPUs with cuDF:
    • Prepare real data for efficient GPU ingestion with RAPIDS cuDF.
    • Train a classification model using GPU-accelerated XGBoost and CPU-only XGBoost.
    • Compare and discuss performance and accuracy results for XGBoost using CPUs, GPUs, and GPUs with cuDF.

Training LSTM Models Using Keras and TensorFlow for Time Series

  • Learn how to predict part failures using a deep learning LSTM model with time-series data:
    • Prepare sequenced data for time-series model training.
    • Build and train a deep learning model with LSTM layers using Keras.
    • Evaluate the accuracy of the model.

Training Autoencoders for Anomaly Detection

  • Learn how to predict part failures using anomaly detection with autoencoders:
    • Build and train an LSTM autoencoder.
    • Develop and train a 1D convolutional autoencoder.
    • Experiment with hyperparameters and compare the results of the models.

Assessment and Q&A