What to read after Fundamentals of Deep Learning?

We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating self-driving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But deciphering these breakthroughs often takes a PhD in machine learning and mathematics.

The updated second edition of this book describes the intuition behind these innovations without jargon or complexity. Python-proficient programmers, software engineering professionals, and computer science majors will be able to reimplement these breakthroughs on their own and reason about them with a level of sophistication that rivals some of the best developers in the field.

• Learn the mathematics behind machine learning jargon
• Examine the foundations of machine learning and neural networks
• Manage problems that arise as you begin to make networks deeper
• Build neural networks that analyze complex images
• Perform effective dimensionality reduction using autoencoders
• Dive deep into sequence analysis to examine language
• Explore methods in interpreting complex machine learning models
• Gain theoretical and practical knowledge on generative modeling
• Understand the fundamentals of reinforcement learning