Abstract: 

Obscure until recently, Deep Learning is ubiquitous today across data-driven applications as diverse as machine vision, natural language processing, generative A.I., and superhuman game-playing.

This workshop is an introduction to Deep Learning that brings high-level theory to life with interactive examples featuring PyTorch, TensorFlow 2, and Keras — all three of the principal Python libraries for Deep Learning. Essential theory will be covered in a manner that provides students with a complete intuitive understanding of Deep Learning’s underlying foundations.

Paired with hands-on code demos in Jupyter notebooks as well as strategic advice for overcoming common pitfalls, this foundational knowledge will empower individuals with no previous understanding of artificial neural networks to train Deep Learning models following all of the latest best-practices.

Session Outline:

Lesson 1: The Unreasonable Effectiveness of Deep Learning
Training Overview
Introduction to Neural Networks and Deep Learning
The Deep Learning Families and Libraries

Lesson 2: Essential Deep Learning Theory
The Cart Before the Horse: A Shallow Neural Network
Learning with Artificial Neurons
TensorFlow Playground—Visualizing a Deep Net in Action

Lesson 3: Deep Learning with PyTorch and TensorFlow 2
Revisiting our Shallow Neural Network
Deep Nets
Convolutional Neural Networks

Bio: 

Jon Krohn is Co-Founder and Chief Data Scientist at the machine learning company Nebula. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He is also the host of SuperDataScience, the data science industry’s most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at leading universities and conferences, as well as via his award-winning YouTube channel. He holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010.