Xie Liang et al.: Keras Deep Learning in Practice

The 2006 machine-driving industry was facing a major turning point. Professor Muslana Rakundiwen of the machine-during field at the University of Toronto, Canada, and his student, has published a paper on "Kun" about "Bammmahabe #abets.". From the publication of this paper until now, Deeping has moved forward rapidly. The book uses the current trend-based modeling action dynamics to describe the subject of deep-down learning. We're using the word "urons" because we've considered three ways. First, the gereas contains a wide variety of common deep-diving modules that can be applied to most office environments. Second, in principle, it's a highly abstract deep-diving program, and it'll be easy to learn. The bottom line of the week is the CENTK, the EnsorFame or Theno to proceed with the calculations. Third, as an application worker, we need to focus on how to convert the problem of commerce or fairness into a suitable model and how to prepare data, analyze the pros and cons of models, and make sure that users are able to focus their work logic away from specific matrix operations and derivatives. This is a real forgiveness that systematically describes how we use the gereas Deeping framework to model neural networks. Data scientist, machine-murning engineer, AI applied engineer and job-work, are suitable for the unemployed who should be conducting predictive modeling and rounds analysis.

Korean title: 케라스 딥러닝 실전

Korean author: 씨에량 외

Korean publisher: 광문각

ISBN-13: 9788970939643