Neural Network Architectures

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Neural Network Architectures

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Author : Judith E. Dayhoff
language : en
Publisher: Itp New Media
Release Date : 1996

Neural Network Architectures written by Judith E. Dayhoff and has been published by Itp New Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computer architecture categories.

Turing S Connectionism

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Author : Christof Teuscher
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Turing S Connectionism written by Christof Teuscher and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.

Christof Teuscher revives, analyzes, and simulates Turing's ideas, applying them to different types of problems, and building and training Turing's machines using evolutionary algorithms. In a little known paper entitled 'Intelligent Machinery' Turing investigated connectionist networks, but his work was dismissed as a 'schoolboy essay'and it was left unpublished until 1968, 14 years after his death. This is not a book about today's (classical) neural networks, but about the neuron network-like structures proposed by Turing. One of its novel features is that it actually goes beyond Turing's ideas by proposing new machines. The book also contains a Foreward by B. Jack Copeland and D. Proudfoot.

Deep Learning Architectures

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Author : Ovidiu Calin
language : en
Publisher: Springer Nature
Release Date : 2020-02-13

Deep Learning Architectures written by Ovidiu Calin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-13 with Mathematics categories.

This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.

Neural Network Architectures Examples Using Matlab

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Author : J. Smith
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-02-26

Neural Network Architectures Examples Using Matlab written by J. Smith and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-26 with Computer architecture categories.

MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more important features are the following: - Deep learning, including convolutional neural networks and autoencoders - Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) - Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) - Unsupervised learning algorithms, including self-organizing maps and competitive layers - Apps for data-fitting, pattern recognition, and clustering - Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance - Simulink(R) blocks for building and evaluating neural networks and for control systems applications Neural networks are composed of simple elements operating in parallel. These elements are inspired by biological nervous systems. As in nature, the connections between elements largely determine the network function. You can train a neural network to perform a particular function by adjusting the values of the connections (weights) between elements.

Number Systems For Deep Neural Network Architectures

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Author : Ghada Alsuhli
language : en
Publisher: Springer Nature
Release Date : 2023-09-01

Number Systems For Deep Neural Network Architectures written by Ghada Alsuhli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-01 with Technology & Engineering categories.

This book provides readers a comprehensive introduction to alternative number systems for more efficient representations of Deep Neural Network (DNN) data. Various number systems (conventional/unconventional) exploited for DNNs are discussed, including Floating Point (FP), Fixed Point (FXP), Logarithmic Number System (LNS), Residue Number System (RNS), Block Floating Point Number System (BFP), Dynamic Fixed-Point Number System (DFXP) and Posit Number System (PNS). The authors explore the impact of these number systems on the performance and hardware design of DNNs, highlighting the challenges associated with each number system and various solutions that are proposed for addressing them.

Neural Networks And Deep Learning

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Author : Pat Nakamoto
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-06-30

Neural Networks And Deep Learning written by Pat Nakamoto and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-30 with categories.

What's Inside? This includes 3 manuscripts: Book 1: Neural Networks & Deep Learning: Deep Learning explained to your granny - A visual introduction for beginners who want to make their own Deep Learning Neural Network... What you will gain from this book: * A deep understanding of how Deep Learning works * A basics comprehension on how to build a Deep Neural Network from scratch Who this book is for: * Beginners who want to approach the topic, but are too afraid of complex math to start! * Two main Types of Machine Learning Algorithms * A practical example of Unsupervised Learning * What are Neural Networks? * McCulloch-Pitts's Neuron * Types of activation function * Types of network architectures * Learning processes * Advantages and disadvantages * Let us give a memory to our Neural Network * The example of book writing Software * Deep learning: the ability of learning to learn * How does Deep Learning work? * Main architectures and algorithms * Main types of DNN * Available Frameworks and libraries * Convolutional Neural Networks * Tunnel Vision * Convolution * The right Architecture for a Neural Network * Test your Neural Network * A general overview of Deep Learning * What are the limits of Deep Learning? * Deep Learning: the basics * Layers, Learning paradigms, Training, Validation * Main architectures and algorithms * Models for Deep Learning * Probabilistic graphic models * Restricted Boltzmann Machines * Deep Belief Networks Book2: Deep Learning: Deep Learning explained to your granny - A guide for Beginners... What's Inside? * A general overview of Deep Learning * What are the limits of Deep Learning? * Deep Learning: the basics * Layers, Learning paradigms, Training, Validation * Main architectures and algorithms * Convolutional Neural Networks * Models for Deep Learning * Probabilistic graphic models * Restricted Boltzmann Machines * Deep Belief Networks * Available Frameworks and libraries * TensorFlow Book 3: Big Data: The revolution that is transforming our work, market and world... "Within 2 days we produce the same amount of data generated by at the beginning of the civilization until 2003," said Eric Schmidt in 2010. According to IBM, by 2020 the world will have generated a mass of data on the order of 40 zettabyte (1021Byte). Just think, for example, of digital content such as photos, videos, blogs, posts, and everything that revolves around social networks; only Facebook marks 30 billion pieces of content each month shared by its users. The explosion of social networks, combined with the emergence of smartphones, justifies the fact that one of the recurring terms of recent years in the field of innovation, marketing and IT is "Big Data." The term Big Data indicates data produced in massive quantities, with remarkable rapidity and in the most diverse formats, which require technologies and resources that go far beyond conventional data management and storage systems. In order to obtain from the use of this data the maximum results in the shortest possible time or even in real time, specific tools with high computing capabilities are necessary. But what does the Big Data phenomenon mean? Is the proliferation of data simply the sign of an increasingly invasive world? Or is there something more to it? Pat Nakamoto will guide you through the discovery of the world of Big data, which, according to experts, in the near future could become the new gold or oil, in what is a real Data Driven economy.

Neural Network Architectures For Artificial Intelligence

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Author : American Association for Artificial Intelligence
language : en
Publisher:
Release Date : 1988-08-01

Neural Network Architectures For Artificial Intelligence written by American Association for Artificial Intelligence and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988-08-01 with Artificial intelligence categories.

Python Deep Learning

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Author : Ivan Vasilev
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-01-16

Python Deep Learning written by Ivan Vasilev and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-16 with Computers categories.

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries Key Features Build a strong foundation in neural networks and deep learning with Python libraries Explore advanced deep learning techniques and their applications across computer vision and NLP Learn how a computer can navigate in complex environments with reinforcement learning Book DescriptionWith the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.What you will learn Grasp the mathematical theory behind neural networks and deep learning processes Investigate and resolve computer vision challenges using convolutional networks and capsule networks Solve generative tasks using variational autoencoders and Generative Adversarial Networks Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models Explore reinforcement learning and understand how agents behave in a complex environment Get up to date with applications of deep learning in autonomous vehicles Who this book is for This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.

Study Of Neural Network Architecture And Applicability

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Author : Al-Amin Shohag
language : en
Publisher:
Release Date : 2020-09-22

Study Of Neural Network Architecture And Applicability written by Al-Amin Shohag and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-22 with categories.

The book will provide you with the best details about, development and application of different types of Artificial neural network architectures and provides a comparison among machine learning, image processing algorithms for the task of classification of categorical data and image classification, detection, recognition and scene understanding from images by using deep learning ANN algorithms.

Hands On Deep Learning Architectures With Python

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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-04-30

Hands On Deep Learning Architectures With Python written by Yuxi (Hayden) Liu and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-30 with Computers categories.

Concepts, tools, and techniques to explore deep learning architectures and methodologies Key FeaturesExplore advanced deep learning architectures using various datasets and frameworksImplement deep architectures for neural network models such as CNN, RNN, GAN, and many moreDiscover design patterns and different challenges for various deep learning architecturesBook Description Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems. Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations. By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world. What you will learnImplement CNNs, RNNs, and other commonly used architectures with PythonExplore architectures such as VGGNet, AlexNet, and GoogLeNetBuild deep learning architectures for AI applications such as face and image recognition, fraud detection, and many moreUnderstand the architectures and applications of Boltzmann machines and autoencoders with concrete examples Master artificial intelligence and neural network concepts and apply them to your architectureUnderstand deep learning architectures for mobile and embedded systemsWho this book is for If you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book