Abstract: 

Relatively obscure a few short years ago, Deep Learning is ubiquitous today across data-driven applications as diverse as machine vision, natural language processing, and super-human game-playing.

This Deep Learning primer brings the revolutionary machine-learning approach behind contemporary artificial intelligence to life with interactive demos featuring TensorFlow 2.0, the major, cutting-edge revision of the world's most popular Deep Learning library.

To facilitate an intuitive understanding of Deep Learning’s artificial-neural-network foundations, the essential theory will be introduced visually and pragmatically. Paired with tips for overcoming common pitfalls and hands-on Python code run-throughs provided in straightforward Jupyter notebooks, this foundational knowledge empowers you to build powerful state-of-the-art Deep Learning models.

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 in TensorFlow 2.0
Learning with Artificial Neurons
TensorFlow Playground—Visualizing a Deep Net in Action

Lesson 3: Deep Learning with TensorFlow 2.0
Revisiting our Shallow Neural Network
Deep Nets in TensorFlow
Convolutional Neural Networks in TensorFlow

Bio: 

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He presents an acclaimed series of tutorials published by Addison-Wesley, including Deep Learning with TensorFlow and Deep Learning for Natural Language Processing. Jon teaches his deep learning curriculum in-classroom at the New York City Data Science Academy and guest lectures at Columbia University. He holds a doctorate in neuroscience from the University of Oxford and, since 2010, has been publishing on machine learning in leading peer-reviewed journals. His book, Deep Learning Illustrated, is being published by Pearson in 2019.