Hwang Yun-gu et al.: Numerical Optimization and Deep Learning with TensorFlow

This book uses TensaurFell, and it deals with the way we learn different deep-running models and the best theory of optimization. There are many mathematical theories about deep-downing, but they explain deep-diving learning around the key problems of optimization. "The Omnipotent and Reinterpretation for Deeping", which was published in the year 2004. There's a huge change in all of the original examples of Tensaple 1 and then the change to Tensupple 2. We've already added important prelearning parts of natural language data, and also videos, natural languages, and table data to help us apply them to real sites and products. The Deeping and the Teachings that have been published so far are either not about optimal theory or too simple. But that's not enough to understand optimal theory. The first thing it does is introduces the optimal theory, and the problem with optimization is to use Tensaple. [This book needs to be taught good]. This book does not explain every detail of Python fundamental grammar and expression. And if you've got good learning, you can actually improve the learning effect. It's just... Understanding Python basic grammar 2. The book is based on the following reader:. It's just... Readers need an understanding of the key theory of deep-downing. Readers who have difficulty reading their paper on Deeping.

Korean title: 텐서플로로 배우는 수치최적화와 딥러닝

Korean author: 황윤구 외

Korean publisher: 남가람북스

ISBN-13: 9791189184087