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Neural Network and Deep Learning 中文翻译版本

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人工智能（AI） 基于项目的人工智能（AI）游乐场。 专案 贡献 欢迎大多数贡献。 联络人 如有任何疑问，请随时与我联系（ ）。

UNIX Network Programming, Volume 1, Second Edition: Networking APIs: Sockets and XTI, Prentice Hall, 1998, ISBN 0-13-490012-X.

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3GPP management and orchestration of 5G networks and network slicing

网络化系统讲义，作者是IEEE控制系统分会的主席。Francesco Bullo is a Professor in the Mechanical Engineering Department at the University of California, Santa Barbara. He received the Laurea degree “summa cum laude” in Electrical Engineering from the University of Padova, Italy, in 1994, and the Ph.D. degree in Control and Dynamical Systems from the California Institute of Technology in 1999. From 1998 to 2004, he was an Assistant Professor with the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign. Since 2004 he has been at University of California, Santa Barbara; he is currently affiliated with the Department of Electrical and Computer Engineering, the Department of Computer Science, and the Center for Control, Dynamical Systems and Computation. Professor Bullos’ research focuses on modeling, dynamics and control of multi-agent network systems, with applications to robotic coordination, power systems, distributed computing and social networks. Previous work includes contributions to geometric control, Lagrangian systems, vehicle routing, and motion planning. Professor Bullo has published more than 270 papers in international journals, books, and refereed conferences. He is the coauthor, with Andrew D. Lewis, of the book “Geometric Control of Mechanical Systems” (Springer, 2004, 0-387-22195-6), with Jorge Cortés and Sonia Martínez, of the book “Distributed Control of Robotic Networks” (Princeton, 2009, 978-0-691-14195-4), and with Stephen L. Smith of the book “Lectures on Robotics Planning and Kinematics” (SIAM, 2016, under review); his “Lectures on Network Systems” (CreateSpace, 2018, 978-1986425643) is available on his website. Professor Bullo is a Fellow of IEEE and IFAC. He is currently a Distinguished Lecturer of the IEEE Control Systems Society. He received the 2018 Distinguished Scientist Award by the Chinese Academy of Sciences. His articles received the 2008 CSM Outstanding Paper Award from IEEE CSS, the 2011 Hugo Schuck Best Paper Award from AAC

神经网络的时间序列分析 重点比较ANN，RNN，LSTM和LSTM在时序分析中的表现 在这个项目中，我建立并比较了四种类型的ANN模型：具有Attention的完全连接的ANN，RNN，LSTM，LSTM。 有两个包含时间序列的数据集。 目的是建立深度神经网络，该网络可以学习数据中的时间模式并预测未来观察的价值。 对于那些模型，我比较了预测的准确性和训练过程的速度。 请参考Report.pdf了解详细说明和参考。 为了构建神经网络，我使用python keras库。 为了实现注意力机制，我使用了Christos Baziotis的。

破产机器学习 破产数据研究的目的是为给定数据确定预测破产的最佳分类方法。 破产数据是从COMPUSTAT收集的1980年至2000年的数据，其中有5436个观察值和13个变量。 9个基于会计的变量和1个市场变量是：R1：WC / TA，营运资金/总资产R2：RE / TA，未分配利润/总资产R3：EBIT / TA，息税前利润/总资产R4：ME / TL，权益/总负债的市场价值R5：S / TA，销售/总资产R6：TL / TA，总负债/总资产R7：CA / CL，流动资产/流动负债R8：NI / TA，净收入/总资产R9：破产成本，对数（销售）R10：市值，对数（绝对（价格）*流通股数/ 1000） 对于本研究，由于没有明显的破产趋势，因此可以假定可以将多年来的数据汇总在一起并进行研究。 在这13个变量中，其中一个是“ DLRSN”-一种表示默认值的分类变量，即预测的因变量。 总体而