Fundamentals of Deep Learning (NV-FUND-DL)

• 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

Module 1:  Introduction

• Meet the instructor.
• Create an account.

Module 2: The Mechanics of Deep Learning

• 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

Module 3: Pre-trained Models and Large Language Models

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.

Module 4: 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.

Module 5: 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.

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.