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Powerful, independent recipes to build deep learning models in different application areas using R librariesAbout This BookMaster intricacies of R deep learning packages such as mxnet & tensorflowLearn application on deep learning in different domains using practical examples from text, image and speechGuide to set-up deep learning models using CPU and GPUWho This Book Is ForData science professionals or analysts who have performed machine learning tasks and now want to explore deep learning and want a quick reference that could address the pain points while implementing deep learning. Those who wish to have an edge over other deep learning professionals will find this book quite useful.What You Will LearnBuild deep learning models in different application areas using TensorFlow, H2O, and MXnet.Analyzing a Deep boltzmann machineSetting up and Analysing Deep belief networksBuilding supervised model using various machine learning algorithmsSet up variants of basic convolution functionRepresent data using Autoencoders.Explore generative models available in Deep Learning.Discover sequence modeling using Recurrent netsLearn fundamentals of Reinforcement LeaningLearn the steps involved in applying Deep Learning in text miningExplore application of deep learning in signal processingUtilize Transfer learning for utilizing pre-trained modelTrain a deep learning model on a GPUIn DetailDeep Learning is the

This book is a practical guide to classification learning systems and their applications. These computer programs learn from sample data and make predictions for new cases, sometimes exceeding the performance of humans.Practical learning systems from statistical pattern recognition, neural networks, and machine learning are presented. The authors examine prominent methods from each area, using an engineering approach and taking the practitioner's viewpoint. Intuitive explanations with a minimum of mathematics make the material accessible to anyone--regardless of experience or special interests.The underlying concepts of the learning methods are discussed with fully worked-out examples: their strengths and weaknesses, and the estimation of their future performance on specific applications. Throughout, the authors offer their own recommendations for selecting and applying learning methods such as linear discriminants, back-propagation neural networks, or decision trees. Learning systems are then contrasted with their rule-based counterparts from expert systems.

Powerful, independent recipes to build deep learning models in different application areas using R librariesAbout This BookMaster intricacies of R deep learning packages such as mxnet & tensorflowLearn application on deep learning in different domains using practical examples from text, image and speechGuide to set-up deep learning models using CPU and GPUWho This Book Is ForData science professionals or analysts who have performed machine learning tasks and now want to explore deep learning and want a quick reference that could address the pain points while implementing deep learning. Those who wish to have an edge over other deep learning professionals will find this book quite useful.What You Will LearnBuild deep learning models in different application areas using TensorFlow, H2O, and MXnet.Analyzing a Deep boltzmann machineSetting up and Analysing Deep belief networksBuilding supervised model using various machine learning algorithmsSet up variants of basic convolution functionRepresent data using Autoencoders.Explore generative models available in Deep Learning.Discover sequence modeling using Recurrent netsLearn fundamentals of Reinforcement LeaningLearn the steps involved in applying Deep Learning in text miningExplore application of deep learning in signal processingUtilize Transfer learning for utilizing pre-trained modelTrain a deep learning model on a GPUIn DetailDeep Learning is the

Since World War II, a group of scientists has been attempting to understand the human nervous system and to build computer systems that emulate the brain's abilities. Many of the early workers in this field of neural networks came from cybernetics; others came from neuroscience, physics, electrical engineering, mathematics, psychology, even economics. In this collection of interviews, those who helped to shape the field share their childhood memories, their influences, how they became interested in neural networks, and what they see as its future.The subjects tell stories that have been told, referred to, whispered about, and imagined throughout the history of the field. Together, the interviews form a Rashomon-like web of reality. Some of the mythic people responsible for the foundations of modern brain theory and cybernetics, such as Norbert Wiener, Warren McCulloch, and Frank Rosenblatt, appear prominently in the recollections. The interviewees agree about some things and disagree about more. Together, they tell the story of how science is actually done, including the false starts, and the Darwinian struggle for jobs, resources, and reputation. Although some of the interviews contain technical material, there is no actual mathematics in the book.Contributors : James A. Anderson, Michael Arbib, Gail Carpenter, Leon Cooper, Jack Cowan, Walter Freeman, Stephen Grossberg, Robert Hecht-Neilsen, Geoffrey Hinton, Teuvo Kohonen, Bart Kosko, Jerome Lettvin, Carver Mead, David Rumelhart, Terry Sejnowski, Paul Werbos, Bernard Widrow.

Since World War II, a group of scientists has been attempting to understand the human nervous system and to build computer systems that emulate the brain's abilities. Many of the early workers in this field of neural networks came from cybernetics; others came from neuroscience, physics, electrical engineering, mathematics, psychology, even economics. In this collection of interviews, those who helped to shape the field share their childhood memories, their influences, how they became interested in neural networks, and what they see as its future.The subjects tell stories that have been told, referred to, whispered about, and imagined throughout the history of the field. Together, the interviews form a Rashomon-like web of reality. Some of the mythic people responsible for the foundations of modern brain theory and cybernetics, such as Norbert Wiener, Warren McCulloch, and Frank Rosenblatt, appear prominently in the recollections. The interviewees agree about some things and disagree about more. Together, they tell the story of how science is actually done, including the false starts, and the Darwinian struggle for jobs, resources, and reputation. Although some of the interviews contain technical material, there is no actual mathematics in the book.ContributorsJames A. Anderson, Michael Arbib, Gail Carpenter, Leon Cooper, Jack Cowan, Walter Freeman, Stephen Grossberg, Robert Hecht-Neilsen, Geoffrey Hinton, Teuvo Kohonen, Bart Kosko, Jerome Lettvin, Carver Mead, David Rumelhart, Terry Sejnowski, Paul Werbos, Bernard Widrow

Since World War II, a group of scientists has been attempting to understand the human nervous system and to build computer systems that emulate the brain's abilities. Many of the early workers in this field of neural networks came from cybernetics; others came from neuroscience, physics, electrical engineering, mathematics, psychology, even economics. In this collection of interviews, those who helped to shape the field share their childhood memories, their influences, how they became interested in neural networks, and what they see as its future.The subjects tell stories that have been told, referred to, whispered about, and imagined throughout the history of the field. Together, the interviews form a Rashomon-like web of reality. Some of the mythic people responsible for the foundations of modern brain theory and cybernetics, such as Norbert Wiener, Warren McCulloch, and Frank Rosenblatt, appear prominently in the recollections. The interviewees agree about some things and disagree about more. Together, they tell the story of how science is actually done, including the false starts, and the Darwinian struggle for jobs, resources, and reputation. Although some of the interviews contain technical material, there is no actual mathematics in the book.Contributors : James A. Anderson, Michael Arbib, Gail Carpenter, Leon Cooper, Jack Cowan, Walter Freeman, Stephen Grossberg, Robert Hecht-Neilsen, Geoffrey Hinton, Teuvo Kohonen, Bart Kosko, Jerome Lettvin, Carver Mead, David Rumelhart, Terry Sejnowski, Paul Werbos, Bernard Widrow.

In plain language, Virtual Trading, shows you how to proceed from data collection to system development to actual trading. For traders who want to stay on the cutting edge of market technology, Virtual Trading is a must read. Featuring contributions from the leading experts in the field, Virtual Trading provides in-depth information on every important aspect of artificial intelligence in trading. Highlights include: Synergistic market analysis using neural networks by Lou Mendelsohn; Developing a market-timing system using genetic algorithms by Casimir Klimasauskas; Neural networkds and stock market valuation by John Keal; Applying chaos theory to a neural network by Joseph Shepard; Developing a trading system that uses Al by Mark Jurik; Neural network techniques for time series analysis by Peter Davies.

This portable hitting cage is foldable and suitable for indoor/outdoor use in various locations like the backyard, park, garage or poolside. Ideal for extended practice sessions anytime, anywhere. The cage design helps contain golf balls and prevents damage to your property or other surrounding areas.

This portable hitting cage is foldable and suitable for indoor/outdoor use in various locations like the backyard, park, garage or poolside. Ideal for extended practice sessions anytime, anywhere. The cage design helps contain golf balls and prevents damage to your property or other surrounding areas.

This portable hitting cage is foldable and suitable for indoor/outdoor use in various locations like the backyard, park, garage or poolside. Ideal for extended practice sessions anytime, anywhere. The cage design helps contain golf balls and prevents damage to your property or other surrounding areas.

A safety bollard is being used for a variety of places, from store fronts to the manufacturing floor. This unit stands vertical to property, products and personnel. Colorful surface provides enhanced visibility and awareness of the surroundings. Material: Steel.

A safety bollard is being used for a variety of places, from store fronts to the manufacturing floor. This unit stands vertical to property, products and personnel. Colorful surface provides enhanced visibility and awareness of the surroundings. Material: Steel.

A safety bollard is being used for a variety of places, from store fronts to the manufacturing floor. This unit stands vertical to property, products and personnel. Colorful surface provides enhanced visibility and awareness of the surroundings. Material: Steel.

A safety bollard is being used for a variety of places, from store fronts to the manufacturing floor. This unit stands vertical to property, products and personnel. Colorful surface provides enhanced visibility and awareness of the surroundings. Material: Steel.

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.

Even without prior experience, this easy-to-follow primer of neural network architectures will give both scientists and other professionals an overview of neural net theory, as well as coverage of important new applications.

SVOPES Barricade Backstop Net A reliable and versatile sports barrier for a variety of ball games. The SVOPES Barricade Backstop Net is perfect for protecting your surroundings while playing baseball, softball, tennis, soccer, and more. Its high-quality design ensures stability and durability, making it ideal for backyards, lawns, or sports fields. Whether you're practicing alone or with a group, this easy-to-set-up and portable net keeps errant balls from causing damage to windows or property. Multi-Sport Compatibility. Durable Construction. Quick Setup. Reliable Stability. Portable Design.

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.

Providing detailed examples of simple applications, this new book introduces the use of neural networks. It covers simple neural nets for pattern classification; pattern association; neural networks based on competition; adaptive-resonance theory; and more. For professionals working with neural networks.

Cover classical algorithms commonly used as artificial intelligence techniques and program agile artificial intelligence applications using Pharo. This book takes a practical approach by presenting the implementation details to illustrate the numerous concepts it explains. Along the way, you’ll learn neural net fundamentals to set you up for practical examples such as the traveling salesman problem and cover genetic algorithms including a fun zoomorphic creature example. Furthermore, Practical Agile AI with Pharo finishes with a data classification application and two game applications including a Pong-like game and a Flappy Bird-like game. This book is informative and fun, giving you source code to play along with. You’ll be able to take this source code and apply it to your own projects.  What You Will LearnUse neurons, neural networks, learning theory, and moreWork with genetic algorithms Incorporate neural network principles when working towards neuroevolution Include neural network fundamentals when building three Pharo-based applicationsWho This Book Is ForCoders and data scientists who are experienced programmers and have at least some prior experience with AI or deep learning. They may be new to Pharo programming, but some prior experience with it would be helpful.