Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

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