الصفحة 2
الصفحة 2
Applied Deep Learning with TensorFlow 2 : Learn to Implement Advanced Deep Learning Techniques with Python
Focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This book is designed so that you can focus on the parts you are interested in. You will explore topics as regularization, optimizers, optimization, metric analysis, and hyper-parameter tuning. In addition, you will learn the fundamentals ideas behind autoencoders and generative adversarial networks. All the code presented in the book will be available in the form of Jupyter notebooks which would allow you to try out all examples and extend them in interesting ways. A companion online book is available with the complete code for all examples discussed in the book and additional material more related to TensorFlow and Keras. All the code will be available in Jupyter notebook format and can be opened directly in Google Colab (no need to install anything locally) or downloaded on your own machine and tested locally. You will: Understand the fundamental concepts of how neural networks work / Learn the fundamental ideas behind autoencoders and generative adversarial networks / Be able to try all the examples with complete code examples that you can expand for your own projects / Have available a complete online companion book with examples and tutorials.
Advanced technique and future perspective for next generation optical fiber communications
Optical fiber communication industry has gained unprecedented opportunities and achieved rapid progress in recent years. However, with the increase of data transmission volume and the enhancement of transmission demand, the optical communication field still needs to be upgraded to better meet the challenges in the future development. Artificial intelligence technology in optical communication and optical network is still in its infancy, but the existing achievements show great application potential. In the future, with the further development of artificial intelligence technology, AI algorithms combining channel characteristics and physical properties will shine in optical communication. This reprint introduces some recent advances in optical fiber communication and optical network, and provides alternative directions for the development of the next generation optical fiber communication technology.
Advanced Fuzzy Logic Technologies in Industrial Applications
Addresses the problem by introducing a dynamic, on-line fuzzy inference system. In this system membership functions and control rules are not determined until the system is applied and each output of its lookup table is calculated based on current inputs. The tuning process is a major focus in this volume because it is the most difficult stage in fuzzy control application. Using new methods such as µ-law technique, histogram equalization and the Bezier-based method, all detailed here, the tuning process can be significantly simplified and control performance improved. The other great strength of this book lies in the range and contemporaneity of its applications and examples which include: laser tracking and control; robot calibration; image processing and pattern recognition; medical engineering; audio systems; autonomous underwater vehicles and data mining.
Advanced data mining and applications ; Vol. 4093 : 2nd International Conference, ADMA 2006, Xi'an, China, August 14-16, 2006, Proceedings
This book constitutes the refereed proceedings of the Second International Conference on Advanced Data Mining and Applications, ADMA 2006, held in Xi'an, China in August 2006. The 41 revised full papers and 74 revised short papers presented together with 4 invited papers were carefully reviewed and selected from 515 submissions. The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, text mining, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, security and privacy issues, spatial data mining, and streaming data mining.
Adaptive Voltage Control in Power Systems : Modeling, Design and Applications
Adaptive Voltage Control in Power Systems, a self-contained blend of theory and novel application, is an in-depth treatment of such adaptive control schemes. The reader moves from power-system-modelling problems through illustrations of the main adaptive control systems (self-tuning, model-reference and nonlinearities compensation) to a detailed description of design methods: Kalman filtering, parameter-identification algorithms and discrete-time controller design are all represented. Case studies address applications issues in the implementation of adaptive voltage control.
Adaptive Techniques for Mixed Signal System on Chip
Adaptive Techniques for Mixed Signal Sytem on Chip discusses the concept of adaptation in the context of analog and mixed signal design along with different adaptive architectures used to control any system parameter. The first part of the book gives an overview of the different elements that are normally used in adaptive designs including tunable elements as well as voltage, current, and time references with an emphasis on the circuit design of specific blocks such as voltage-controlled transconductors, offset comparators, and a novel technique for accurate implementation of on chip resistors. While the first part of the book addresses adaptive techniques at the circuit and block levels, the second part discusses adaptive equalization architectures employed to minimize the impact of ISI (Intersymbol Interference) on the quality of received data in high-speed wire line transceivers. It presents the implementation of a 125Mbps transceiver operating over a variable length of Category 5 (CAT-5) Ethernet cable as an example of adaptive equalizers.
Adaptive Techniques for Dynamic Processor Optimization : Theory and Practice
This book discusses the different approaches and responses to adaptive techniques used for processor power, frequency and functionality optimization. Adaptive Techniques for Dynamic Processor Optimization: Theory and Practice includes chapter contributions that explore promising approaches and present the supporting data.
Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods
This book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib ution. When the learning process is organized according to these steps, performed together one after the other or separately, one may expect to discover models that generalize well.
