Explore H2O Hydrogen Torch, a powerful application for building deep learning models without coding, in this 35-minute video tutorial. Learn how to optimize and simplify the training process for various computer vision and natural language tasks, including image classification, object detection, semantic segmentation, and text analysis. Follow along as the presenter demonstrates launching the H2O Hydrogen Torch cluster, importing datasets, and conducting experiments for both novice and expert users. Gain insights into the prediction pipeline, uploading test datasets, interpreting results, and understanding Python and MLOps-based deployment options. Perfect for data scientists of all levels looking to streamline their deep learning workflows.
Overview
Syllabus
- Video Start
- H2O Hydrogen Torch Intro
- Dry run or Test run Starts
- Launching H2O Hydrogen Torch Cluster
- Importing Dataset
- Starting first experiment as Novice or Beginners
- Experiment Understanding and validation
- Starting experiment as Expert or Skilled Engineer
- Understanding Prediction pipeline with Hydrogen Torch
- Uploading test dataset for the prediction
- Starting local Prediction with uploaded dataset
- Understanding Prediction results
- Understanding Python and MLOps based prediction deployment
- Credits
Taught by
Prodramp
