Java How to Program，11 / e，早期对象版本 源代码 这些文件仅供您个人使用，不得重新分发或重新发布。 如有任何疑问，请在“问题”标签中打开一个问题，或给我们发送电子邮件：在deitel dot com上发送deitel。 第25章（您手动输入到Jshell中的代码段）或第33章（ATM代码在第34章中）都没有代码。 Deitel＆Associates，Inc和Pearson Education，Inc.版权所有1992-2017。保留所有权利。 本书的作者和出版者已竭尽所能编写本书。 这些工作包括对理论和程序的开发，研究和测试，以确定其有效性。 对于这些程序或本书中包含的文档，作者和发行者不作任何形式的明示或暗示的保证。 在任何情况下，作者，出版商均不对与提供，执行或使用这些程序有关的或由其引起的附带或间接损失负责。

使用Python + Kivy 开发应用程序。 Kivy 是一个跨平台的GUI 支持Window、Linux、Mac、Android

Preface Deep learning is a fascinating field. Artificial neural networks have been around for a long time, but something special has happened in recent years. The mixture of new faster hardware, new techniques and highly optimized open source libraries allow very large networks to be created with frightening ease. This new wave of much larger and much deeper neural networks are also impressively skillful on a range of problems. I have watched over recent years as they tackle and handily become state-of-the-art across a range of difficult problem domains. Not least object recognition, speech recognition, sentiment classification, translation and more. When a technique comes a long that does so well on such a broad set of problems, you have to pay attention. The problem is where do you start with deep learning? I created this book because I thought that there was no gentle way for Python machine learning practitioners to quickly get started developing deep learning models. In developing the lessons in this book, I chose the best of breed Python deep learning library called Keras that abstracted away all of the complexity, ruthlessly leaving you an API containing only what you need to know to efficiently develop and evaluate neural network models. This is the guide that I wish I had when I started apply deep learning to machine learning problems. I hope that you find it useful on your own projects and have as much fun applying deep learning as I did in creating this book for you.

Software Architecture with Python(pdf+epub+mobi+code_files).zip

Learning Concurrency in Python(pdf+epub+mobi+code_files).zip

Learning Python Application Development Learning Python Application Development

Java 11 Cookbook, 2nd Edition epub + Source Code

Visual C++数据库开发典型模块与实例精讲code

对于GC-LDPC码，目标解码方案是两阶段解码方案，即本地解码阶段和全局解码阶段。 对于这两个阶段，我们发现对数域信念传播（BP）算法的直接应用都会导致错误。 因此，我们提出了一种改进的对数域BP算法，并将其用于两阶段解码方案。 由于两相解码方案具有较大的增益损耗，因此我们提出一种改进的两相解码方案，以进一步加快收敛速度​​。 仿真结果表明，与两相解码器相比，改进的两相解码器具有约0.2 dB的增益。 此外，与整个解码器相比，它还可以将高SNR的复杂度降低33.4％。

edk2-code-3.5.0(1)