Fundamentals of Deep Learning Training
Course Authors
Fundamentals of Deep Learning Training Hands-on (Online, Onsite, and Classroom Live)
| Commitment | 1 Day, 7-8 hours a day. |
| Language | English |
| User Ratings | Average User Rating 4.8 See what learners said |
| Price | REQUEST |
| Delivery Options | Instructor-Led Onsite, Online, and Classroom Live |
COURSE OVERVIEW
In this Fundamentals of Deep Learning Training workshop, you’ll learn how deep learning works through hands-on exercises in computer vision and natural language processing. You’ll train deep learning models from scratch, learning tools and tricks to achieve highly accurate results. You’ll also learn to leverage freely available, state-of-the-art pre-trained models to save time and get your deep learning application up and running quickly.
Businesses worldwide are using artificial intelligence to solve their greatest challenges. Healthcare professionals use AI to enable more accurate, faster diagnoses in patients. Retail businesses use it to offer personalized customer shopping experiences. Automakers use it to make personal vehicles, shared mobility, and delivery services safer and more efficient. Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in object detection, speech recognition, and language translation tasks. Using deep learning, computers can learn and recognize patterns from data considered too complex or subtle for expert-written software.
This course is part of the following Certifications:
- NVIDIA-Certified Associate: Generative AI LLMs
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 canceled, and no refund will be issued, regardless of attendance.
WHAT'S INCLUDED?
- 1 day of Fundamentals of Deep Learning Training with an expert instructor
- Fundamentals of Deep Learning Electronic Course Guide
- Certificate of Completion
- 100% Satisfaction Guarantee
RESOURCES
- Fundamentals of Deep Learning – https://www.wiley.com/
- Fundamentals of Deep Learning – https://www.packtpub.com/
- Fundamentals of Deep Learning – https://store.logicaloperations.com/
- Fundamentals of Deep Learning – https://us.artechhouse.com/
- Fundamentals of Deep Learning Training – https://www.amazon.com/
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ADDITIONAL INFORMATION
COURSE OBJECTIVES
Upon completion of this Fundamentals of Deep Learning Training course, participants can:
- 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 framework
CUSTOMIZE IT
- We can adapt this Fundamentals of Deep Learning Training course to your group’s background and work requirements at little to no added cost.
- If you are familiar with some aspects of this Fundamentals of Deep Learning course, we can omit or shorten their discussion.
- We can adjust the emphasis placed on the various topics or build the Fundamentals of Deep Learning course around the mix of technologies of interest to you (including technologies other than those in this outline).
- If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policymaker), and present the Fundamentals of Deep Learning course in a manner understandable to lay audiences.
AUDIENCE/TARGET GROUP
The target audience for this Fundamentals of Deep Learning Training course:
- ALL
CLASS PREREQUISITES
The knowledge and skills that a learner must have before attending this Fundamentals of Deep Learning Training course are:
- 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.
COURSE SYLLABUS
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.
