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Known as The Royalty of the Garden, azaleas light up the spring landscape with a glorious display of lovely blooms year after year. Their vivid colors, profusion of flowers and adaptability to a wide range of soils and climates make them 1 of the most popular flowering shrubs amongst gardeners. Boasting clusters of lightly-scented lavender blooms atop bright green foliage, featured azalea variety Herbert is sure to bring a pop of purple passion to your landscape. Azaleas are ideal for gardens beds, containers, shrub borders, hedges, foundation plants and mass plantings.

Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.

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Neural Networks in C++ An Object-Oriented Framework for Building Connectionist Systems Extremely useful, this valuable guide concentrates on the practical side of building neural network applications. Written with a wealth of useful examples in C++, the book provides you with the nuts-and-bolts guidelines for hands-on development of real-world connectionist systems. Neural Networks in C++ also: Includes a complete C++ class library especially designed for neural network applications Fully covers three major application areas of neural network programming—image recognition, text processing, and forecasting Provides introductory material on neural networks Covers the basics on an object-oriented framework for connectionist systems

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Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow.  Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level.What You Will Learn See how convolutional neural networks and object detection workSave weights and models on diskPause training and restart it at a later stage Use hardware acceleration (GPUs) in your codeWork with the Dataset TensorFlow abstraction and use pre-trained models and transfer learningRemove and add layers to pre-trained networks to adapt them to your specific projectApply pre-trained models such as Alexnet and VGG16 to new datasets   Who This Book Is For Scientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.

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This lecture note volume is mainly about the recent development that connected neural network modeling to the theoretical physics of disordered systems. It gives a detailed account of the (Little-) Hopfield model and its ramifications concerning non-orthogonal and hierarchical patterns, short-term memory, time sequences, and dynamical learning algorithms. It also offers a brief introduction to computation in layered feed-forward networks, trained by back-propagation and other methods. Kohonen's self-organizing feature map algorithm is discussed in detail as a physical ordering process. The book offers a minimum complexity guide through the often cumbersome theories developed around the Hopfield model. The physical model for the Kohonen self-organizing feature map algorithm is new, enabling the reader to better understand how and why this fascinating and somewhat mysterious tool works.

Devoted to local and global analysis of weakly connected systems with applications to neurosciences, this book uses bifurcation theory and canonical models as the major tools of analysis. It presents a systematic and well motivated development of both weakly connected system theory and mathematical neuroscience, addressing bifurcations in neuron and brain dynamics, synaptic organisations of the brain, and the nature of neural codes. The authors present classical results together with the most recent developments in the field, making this a useful reference for researchers and graduate students in various branches of mathematical neuroscience.

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Book is in NEW condition. 1.57

The first truly up-to-date look at the theory and capabilities of nonlinear dynamical systems that take the form of feedforward neural network structures Considered one of the most important types of structures in the study of neural networks and neural-like networks, feedforward networks incorporating dynamical elements have important properties and are of use in many applications. Specializing in experiential knowledge, a neural network stores and expands its knowledge base via strikingly human routes-through a learning process and information storage involving interconnection strengths known as synaptic weights. In Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives, six leading authorities describe recent contributions to the development of an analytical basis for the understanding and use of nonlinear dynamical systems of the feedforward type, especially in the areas of control, signal processing, and time series analysis. Moving from an introductory discussion of the different aspects of feedforward neural networks, the book then addresses: * Classification problems and the related problem of approximating dynamic nonlinear input-output maps * The development of robust controllers and filters * The capability of neural networks to approximate functions and dynamic systems with respect to risk-sensitive error * Segmenting a time series It then sheds light on the application of feedforward neural networks to speech processing, summarizing speech-related techniques, and reviewing feedforward neural networks from the viewpoint of fundamental design issues. An up-to-date and authoritative look at the ever-widening technical boundaries and influence of neural networks in dynamical systems, this volume is an indispensable resource for researchers in neural networks and a reference staple for libraries.

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.   Neural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. This is ideal for professional engineers and research scientists.   Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/   Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.

A large amount of information about the world we live in is supplied by our visual systems. Humans perform the task of vision effortlessly without being aware of this complex process. Hierarchical structures permit successful examination of such intricate operations. This book investigates hierarchical-structured neural networks for vision and image processing tasks and proposes various new neural network models for that purpose. It exploits the capabilities of hierarchical neural networks in a systematic way by considering the similarities to hierarchical structures already in use by computer vision researchers. All issues of hierarchical neural networks are treated in considerable detail; that is, the structure of the network, the representation issue, and learning mechanisms are analyzed theoretically as well as experimentally. Considering the similarity between conventional vision algorithms and hierarchical neural networks not only allows a transfer of knowledge between these two fields, but also gives voice to many new algorithms.

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This book is the companion volume to Rethinking Innateness: A Connectionist Perspective on Development (The MIT Press, 1996), which proposed a new theoretical framework to answer the question "What does it mean to say that a behavior is innate?" The new work provides concrete illustrations―in the form of computer simulations―of properties of connectionist models that are particularly relevant to cognitive development. This enables the reader to pursue in depth some of the practical and empirical issues raised in the first book. The authors' larger goal is to demonstrate the usefulness of neural network modeling as a research methodology.The book comes with a complete software package, including demonstration projects, for running neural network simulations on both Macintosh and Windows 95. It also contains a series of exercises in the use of the neural network simulator provided with the book. The software is also available to run on a variety of UNIX platforms.

Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of the TensorFlow library, this new book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

"Analyzes the behavior, design, and implementation of artificial recurrent neural networks. Offers methods of synthesis for associative memories. Evaluates the qualitative properties and limitations of neural networks. Contains practical applications for optimal system performance."