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, 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
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
Learning with Artificial Neurons
TensorFlow Playground—Visualizing a Deep Net in Action
Lesson 3: Deep Learning with TensorFlow 2
Revisiting our Shallow Neural Network
Deep Nets in TensorFlow
Convolutional Neural Networks in TensorFlow
● Jupyter notebooks;
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.