- Expert Track
- Online
Businesses worldwide are using artificial intelligence to solve their greatest challenges. […] Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Using deep learning, computers can learn and recognize patterns from data that are considered too complex or subtle for expert-written software.
Your Benefits
Participants of this workshop will
- 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
The following topics are covered in this workshop:
- PyTorch
- Convolutional Neural Networks (CNNS)
- Data Augmentation
- Transfer Learning
- Natural Language Processing
For active participation in the workshops and to receive a certificate of attendance signed by NVIDIA, you must register with NVIDIA’s Deep Learning Institute. Further details will be provided during registration. Please note that participation is limited to academic students, staff, and researchers. When registering, ensure that your affiliation is clearly indicated through your email address.
Course Outline
Introduction (30 mins)
- Meet the instructor.
- Create an account at courses.nvidia.com/join
The Mechanics of Deep Learning (3 hours)
- 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.
Break (1 hour)
Pre-trained Models and Large Language Models (1.5 hours)
- 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.
- Use a Large Language Model (LLM) to answer questions based on provided text.
Break (15 mins)
Final Project: Object Classification (1 hours)
- 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 (30 minutes)
- 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.
Instructor
The instructor is Ben Lohmann from hessian.AI.
Language
Workshops will be held in English.
Prerequisites
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.
Suggested materials to satisfy prerequisites: Python Beginner’s Guide.
The technologies used are PyTorch and Pandas.
Regarding hardware requirements you need a Desktop or laptop computer capable of running the latest version of Chrome or Firefox. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated server in the cloud.