2017年的Python深度学习文档及源代码

深度学习 Deep Learning with Python 2017 经典的深度学习教程。强烈推荐下载学习。最新的深度学习教程。 这本书向您介绍了深度学习的桥梁框架如keras实践的学术现状和工业国家之间的差距。 本书简要介绍了深层学习的数学前提和基本原理，使本书成为希望深入学习的软件开发人员的良好起点。还包括了一个简短的深入学习架构的调查。 使用Python进行深入学习还将介绍自动微分和GPU计算的关键概念，而在深入进行大规模实验时，这并不是深入学习的核心概念。 Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments.