Learning Objectives

• 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

Topics Covered

• PyTorch
• Convolutional Neural Networks (CNNS)
• Data Augmentation
• Transfer Learning
• Natural Language Processing

Course Outline

The below is a suggested timeline for the course. Please work with the instructor to find the best timeline for your session.

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.

Duration: 08:00

Level: Technical - Beginner

Subject: Deep Learning

Language: English

Course 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.

Technologies: PyTorch, Pandas

Assessment Type: Skills-based coding assessments evaluate students’ ability to train a deep learning model to high accuracy.

Certificate: Upon successful completion of the assessment, participants will receive an NVIDIA DLI certificate to recognize their subject matter competency and support professional career growth.

Hardware Requirements: 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.

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