Résumé du cours
In this workshop, you’ll learn how to quickly develop and deploy a machine learning model that uses deep learning for computer vision to perform defect classification and other visual recognition tasks. Using NVIDIA’s own real production dataset as an example, this workshop illustrates how the solution can be easily applied to a variety of manufacturing and industrial inspection use cases.
Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.
Moyens d'évaluation :
- Quiz pré-formation de vérification des connaissances (si applicable)
- Évaluations formatives pendant la formation, à travers les travaux pratiques réalisés sur les labs à l’issue de chaque module, QCM, mises en situation…
- Complétion par chaque participant d’un questionnaire et/ou questionnaire de positionnement en amont et à l’issue de la formation pour validation de l’acquisition des compétences
Pré-requis
- Experience with Python; basic understanding of data processing and deep learning.
- To gain experience with Python, we suggest this Python tutorial.
- To get a basic understanding of data processing and deep learning, we suggest DLI’s Fundamentals of Deep Learning.
Objectifs
- Extract meaningful insights from the provided data set using Pandas DataFrame.
- Apply transfer-learning to a deep learning classification model.
- Fine-tune the deep learning model and set up evaluation metrics.
- Deploy and measure model performance.
- Experiment with various inference configurations to optimize model performance.
Suite de parcours
Contenu
Introduction
- Meet the instructor.
- Create an account at courses.nvidia.com/join
Data Exploration and Pre-Processing with DALI
- Learn how to extract valuable insights from a data set and pre-process image data for deep learning model consumption.
- Explore data set with Pandas
- Pre-process data with DALI
- Assess scope for feasibility testing
Efficient Model Training with TAO Toolkit
- Learn how to efficiently train a classification model for the purpose of defect detection using transfer learning techniques
- Train a deep learning model with TAO Toolkit
- Evaluate the accuracy of the model
- Iterate model training to improve accuracy
Model Deployment for Inference
- Learn how to deploy and measure the performance of a deep learning model
- Optimize deep learning models with TensorRT
- Deploy model with Triton Inference Server
- Explore and assess the impact of various inference configurations
Assessment and Q&A
Moyens Pédagogiques :