Deep Learning (with TensorFlow 2)
Deep Learning (with TensorFlow 2)


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
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
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


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.

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