Applying Neural Networks PDF Download
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Author: Kevin Swingler
Publisher: Morgan Kaufmann
ISBN: 9780126791709
Category : Computers
Languages : en
Pages : 348
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Book Description
This book is designed to enable the reader to design and run a neural network-based project. It presents everything the reader will need to know to ensure the success of such a project. The book contains a free disk with C and C++ programs, which implement many of the techniques discussed in the book.
Author: Kevin Swingler
Publisher: Morgan Kaufmann
ISBN: 9780126791709
Category : Computers
Languages : en
Pages : 348
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Book Description
This book is designed to enable the reader to design and run a neural network-based project. It presents everything the reader will need to know to ensure the success of such a project. The book contains a free disk with C and C++ programs, which implement many of the techniques discussed in the book.
Author: Marilyn McCord Nelson
Publisher: Addison Wesley Publishing Company
ISBN:
Category : Computers
Languages : en
Pages : 360
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Book Description
Based on a course given to internal managers at Texas Instruments, this book is an introduction to neural nets for computer science, artificial intelligence and R & D professionals, as well as MIS or DP managers.
Author: Ahmed Fawzy Gad
Publisher: Academic Press
ISBN: 0323909345
Category : Medical
Languages : en
Pages : 302
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Book Description
Introduction to Deep Learning and Neural Networks with PythonTM: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonTM code examples to clarify neural network calculations, by book's end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and PythonTM examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network. - Examines the practical side of deep learning and neural networks - Provides a problem-based approach to building artificial neural networks using real data - Describes PythonTM functions and features for neuroscientists - Uses a careful tutorial approach to describe implementation of neural networks in PythonTM - Features math and code examples (via companion website) with helpful instructions for easy implementation
Author: Grégoire Montavon
Publisher: Springer
ISBN: 3642352898
Category : Computers
Languages : en
Pages : 753
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Book Description
The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.
Author: Hamed Habibi Aghdam
Publisher: Springer
ISBN: 3319575503
Category : Computers
Languages : en
Pages : 303
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Book Description
This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis. Topics and features: explains the fundamental concepts behind training linear classifiers and feature learning; discusses the wide range of loss functions for training binary and multi-class classifiers; illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks; describes two real-world examples of the detection and classification of traffic signs using deep learning methods; examines a range of varied techniques for visualizing neural networks, using a Python interface; provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website. This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice. As no prior background knowledge in the field is required to follow the material, the book is ideal for all students of computer vision and machine learning, and will also be of great interest to practitioners working on autonomous cars and advanced driver assistance systems.
Author: Charu C. Aggarwal
Publisher: Springer
ISBN: 3319944630
Category : Computers
Languages : en
Pages : 512
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Book Description
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
Author: Jeffrey S. Beasley
Publisher: Pearson Education
ISBN: 0133354008
Category : Computers
Languages : en
Pages : 1244
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Book Description
A Practical Guide to Advanced Networking, Third Edition takes a pragmatic, hands-on approach to teaching advanced modern networking concepts from the network administrator’s point of view. Thoroughly updated for the latest networking technologies and applications, the book guides you through designing, configuring, and managing campus networks, connecting networks to the Internet, and using the latest networking technologies. The authors first show how to solve key network design challenges, including data flow, selection of network media, IP allocation, subnetting, and configuration of both VLANs and Layer 3 routed networks. Next, they illuminate advanced routing techniques using RIP/RIPv2, OSPF, IS-IS, EIGRP, and other protocols, and show how to address common requirements such as static routing and route redistribution. You’ll find thorough coverage of configuring IP-based network infrastructure, and using powerful WireShark and NetFlow tools to analyze and troubleshoot traffic. A full chapter on security introduces best practices for preventing DoS attacks, configuring access lists, and protecting routers, switches, VPNs, and wireless networks. This book’s coverage also includes IPv6, Linux-based networking, Juniper routers, BGP Internet routing, and Voice over IP (VoIP). Every topic is introduced in clear, easy-to-understand language; key ideas are reinforced with working examples, and hands-on exercises based on powerful network simulation software. Key Pedagogical Features NET-CHALLENGE SIMULATION SOFTWARE provides hands-on experience with advanced router and switch commands, interface configuration, and protocols–now including RIPv2 and IS-IS WIRESHARK NETWORK PROTOCOL ANALYZER TECHNIQUES and EXAMPLES of advanced data traffic analysis throughout PROVEN TOOLS FOR MORE EFFECTIVE LEARNING, including chapter outlines and summaries WORKING EXAMPLES IN EVERY CHAPTER to reinforce key concepts and promote mastery KEY TERMS DEFINITIONS, LISTINGS, and EXTENSIVE GLOSSARY to help you master the language of networking QUESTIONS, PROBLEMS, and CRITICAL THINKING QUESTIONS to help you deepen your understanding CD-ROM includes Net-Challenge Simulation Software and the Wireshark Network Protocol Analyzer Software examples.
Author: Marilyn McCord Nelson
Publisher: Prentice Hall
ISBN:
Category : Computers
Languages : en
Pages : 378
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Book Description
Author: James A. Anderson
Publisher: MIT Press
ISBN: 9780262510813
Category : Computers
Languages : en
Pages : 680
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Book Description
An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modeling, and at engineers who want to go beyond formal algorithms to applications and computing strategies. It is the only current text to approach networks from a broad neuroscience and cognitive science perspective, with an emphasis on the biology and psychology behind the assumptions of the models, as well as on what the models might be used for. It describes the mathematical and computational tools needed and provides an account of the author's own ideas. Students learn how to teach arithmetic to a neural network and get a short course on linear associative memory and adaptive maps. They are introduced to the author's brain-state-in-a-box (BSB) model and are provided with some of the neurobiological background necessary for a firm grasp of the general subject. The field now known as neural networks has split in recent years into two major groups, mirrored in the texts that are currently available: the engineers who are primarily interested in practical applications of the new adaptive, parallel computing technology, and the cognitive scientists and neuroscientists who are interested in scientific applications. As the gap between these two groups widens, Anderson notes that the academics have tended to drift off into irrelevant, often excessively abstract research while the engineers have lost contact with the source of ideas in the field. Neuroscience, he points out, provides a rich and valuable source of ideas about data representation and setting up the data representation is the major part of neural network programming. Both cognitive science and neuroscience give insights into how this can be done effectively: cognitive science suggests what to compute and neuroscience suggests how to compute it.
Author: Mohit Sewak
Publisher: Packt Publishing Ltd
ISBN: 1788394143
Category : Computers
Languages : en
Pages : 211
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Book Description
One stop guide to implementing award-winning, and cutting-edge CNN architectures Key Features Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques Implement CNN models on image classification, transfer learning, Object Detection, Instance Segmentation, GANs and more Implement powerful use-cases like image captioning, reinforcement learning for hard attention, and recurrent attention models Book Description Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available. Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets. What you will learn From CNN basic building blocks to advanced concepts understand practical areas they can be applied to Build an image classifier CNN model to understand how different components interact with each other, and then learn how to optimize it Learn different algorithms that can be applied to Object Detection, and Instance Segmentation Learn advanced concepts like attention mechanisms for CNN to improve prediction accuracy Understand transfer learning and implement award-winning CNN architectures like AlexNet, VGG, GoogLeNet, ResNet and more Understand the working of generative adversarial networks and how it can create new, unseen images Who this book is for This book is for data scientists, machine learning and deep learning practitioners, Cognitive and Artificial Intelligence enthusiasts who want to move one step further in building Convolutional Neural Networks. Get hands-on experience with extreme datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of deep learning concepts and Python programming language is expected.
