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包括实验题目，代码及运行结果 实验2 银行家算法（2学时） 一、实验目的 理解银行家算法，掌握进程安全性检查的方法及资源分配的方法。 二、实验内容 编写程序实现银行家算法，并验证程序的正确性。 三、实验要求 编制模拟银行家算法的程序，并以下面给出的例子验证所编写的程序的正确性。 例子：某系统有A、B、C、D 4类资源共5个进程（P0、P1、P2、P3、P4）共享，各进程对资源的需求和分配情况如下表所示。 进程 已占资源 最大需求数 A B C D A B C D P0 0 0 1 2 0 0 1 2 P1 1 0 0 0 1 7 5 0 P2 1 3 5 4 2 3 5 6 P3 0 6 3 2 0 6 5 2 P4 0 0 1 4 0 6 5 6 现在系统中A、B、C、D 4类资源分别还剩1、5、2、0个，请按银行家算法回答下列问题： （1）现在系统是否处于安全状态？ （2）如果现在进程P1提出需求（0、4、2、0）个资源的请求，系统能否满足它的请求？

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FCC响应式Web认证课程 我在完成FreeCodeCamp的响应式Web认证课程时编写的自学项目和FreeCodeCamp精通项目的源代码位于该存储库中。

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。

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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.

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