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 Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, which was released by Addison-Wesley in 2019 and became an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and the NYC Data Science Academy, as well as online via O'Reilly, YouTube, and his A4N podcast on A.I. news. Jon holds a doctorate in neuroscience from Oxford and has been publishing on machine learning in leading academic journals since 2010.
