FDLNVNvidiaNV-FDL1.0<p>By participating in this workshop, you&rsquo;ll:</p> <ul> <li>Learn the fundamental techniques and tools required to train a deep learning model</li><li>Gain experience with common deep learning data types and model architectures</li><li>Enhance datasets through data augmentation to improve model accuracy</li><li>Leverage transfer learning between models to achieve efficient results with less data and computation</li><li>Build confidence to take on your own project with a modern deep learning framework</li></ul><p>An understanding of fundamental programming concepts in Python 3, such as functions, loops, dictionaries, and arrays; familiarity with Pandas data structures; and an understanding of how to compute a regression line.</p><h5>Introduction</h5><ul> <li>Meet the instructor.</li><li>Create an account at courses.nvidia.com/join</li></ul><h5>The Mechanics of Deep Learning</h5><p>Explore the fundamental mechanics and tools involved in successfully training deep neural networks:</p> <ul> <li>Train your first computer vision model to learn the process of training.</li><li>Introduce convolutional neural networks to improve accuracy of predictions in vision applications.</li><li>Apply data augmentation to enhance a dataset and improve model generalization.</li></ul><h5>Pre-trained Models and Recurrent Networks</h5><p>Leverage pre-trained models to solve deep learning challenges quickly. Train recurrent neural networks on sequential data:</p> <ul> <li>Integrate a pre-trained image classification model to create an automatic doggy door.</li><li>Leverage transfer learning to create a personalized doggy door that only lets in your dog.</li><li>Train a model to autocomplete text based on New York Times headlines.</li></ul><h5>Final Project: Object Classification</h5><p>Apply computer vision to create a model that distinguishes between fresh and rotten fruit:</p> <ul> <li>Create and train a model that interprets color images.</li><li>Build a data generator to make the most out of small datasets.</li><li>Improve training speed by combining transfer learning and feature extraction.</li><li>Discuss advanced neural network architectures and recent areas of research where students can further improve their skills.</li></ul><h5>Final Review</h5><ul> <li>Review key learnings and answer questions.</li><li>Complete the assessment and earn a certificate.</li><li>Complete the workshop survey.</li><li>Learn how to set up your own AI application development environment.</li></ul>By participating in this workshop, you’ll: - Learn the fundamental techniques and tools required to train a deep learning model - Gain experience with common deep learning data types and model architectures - Enhance datasets through data augmentation to improve model accuracy - Leverage transfer learning between models to achieve efficient results with less data and computation - Build confidence to take on your own project with a modern deep learning frameworkAn understanding of fundamental programming concepts in Python 3, such as functions, loops, dictionaries, and arrays; familiarity with Pandas data structures; and an understanding of how to compute a regression line.Introduction - Meet the instructor. - Create an account at courses.nvidia.com/join The Mechanics of Deep Learning Explore the fundamental mechanics and tools involved in successfully training deep neural networks: - Train your first computer vision model to learn the process of training. - Introduce convolutional neural networks to improve accuracy of predictions in vision applications. - Apply data augmentation to enhance a dataset and improve model generalization. Pre-trained Models and Recurrent Networks Leverage pre-trained models to solve deep learning challenges quickly. Train recurrent neural networks on sequential data: - Integrate a pre-trained image classification model to create an automatic doggy door. - Leverage transfer learning to create a personalized doggy door that only lets in your dog. - Train a model to autocomplete text based on New York Times headlines. Final Project: Object Classification Apply computer vision to create a model that distinguishes between fresh and rotten fruit: - Create and train a model that interprets color images. - Build a data generator to make the most out of small datasets. - Improve training speed by combining transfer learning and feature extraction. - Discuss advanced neural network architectures and recent areas of research where students can further improve their skills. Final Review - Review key learnings and answer questions. - Complete the assessment and earn a certificate. - Complete the workshop survey. - Learn how to set up your own AI application development environment.1 jour995.00995.00500.00995.00995.00420.00690.00995.00