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说明：  数据结构、严蔚敏、课后习题、第四版紫色、电子书(Data structure, Fourth Edition, Yan Weimin, exercises after class)

说明：  神经网络是一种模拟人脑的神经网络，以期能够实现类人工智能的机器学习 技术。 本书揭示神经网络背后的概念，并介绍如何通过Python实现神经网络。全书 分为3章和两个附录。第1章介绍了神经网络中所用到的数学思想。第2章介绍使 用Python实现神经网络，识别手写数字，并测试神经网络的性能。第3章带领读 者进一步了解简单的神经网络，观察已受训练的神经网络内部，尝试进一步改善 神经网络的性能，并加深对相关知识的理解。附录分别介绍了所需的微积分知识 和树莓派知识。 本书适合想要从事神经网络研究和探索的读者学习参考，也适合对人工智 能、机器学习和深度学习等相关领域感兴趣的读者阅读。(Python Neural Network Programming)

说明：  如果你满足以下条件： ? 会使用电脑，但从来没写过程序； ? 还记得初中数学学的方程式和一点点代数知识； ? 想从编程小白变成专业的软件架构师； ? 每天能抽出半个小时学习。 那么你就能学会python(If you meet the following criteria: Can use a computer, but never write a program; Remember junior high school math equations and a little algebra; Want to go from programming whiz to professional software architect; I can spare half an hour to study every day. Then you can learn python)

水润滑轴承计算程序，适用于底部不开槽的轴承(Water lubricated bearing calculation procedure for bearings that are not slotted at the bottom)

Guide to FORTRAN 2008 Programming

说明：  python与机器学习实战教程，机器学习通过Python语言实现，通过大量的实例再现机器学习强大的生命力(Python and Machine Learning Practical Course. Machine Learning is realized by Python Language, and the powerful vitality of machine learning is reappeared through a large number of examples.)

清华大学.计算机组成原理。课堂ppt，帮助自学的人。要自学组成的可以看看(Tsinghua University Computer Organization Principles. Classroom ppt, helping learners. To be able to look at the self-composed)

ansys车辆移动荷载命令流，方便直接输入(ansys vehicle moving load command stream, convenient and direct input)

This book provides a carefully motivated, accessible, and interesting introduction to probability, statistics, and random processes for electrical and computer engineers.The complexity of the systems encountered in engineering practice calls for an understanding of probability concepts and a facility in the use of probability tools.The goal of the introductory course should therefore be to teach both the basic theoretical concepts and techniques for solving problems that arise in practice. The third edition of this book achieves this goal by retaining the proven features of previous editions:

说明：  《小白学SAS》配套材料适读人群 ：SAS用户，统计、数据分析领域的相关工作人员(sas course, Readable population: SAS users, relevant staff in the field of statistics and data analysis)