Detailed Course Outline
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