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SPIRES-BOOKS: FIND KEYWORD DEEP LEARNING *END*INIT* use /tmp/qspiwww.webspi1/1745.21 QRY 131.225.70.96 . find keyword deep learning ( in books using www Cover
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Call number:SPRINGER-2014-9781461487661:ONLINE Show nearby items on shelf
Title:From Casual Stargazer to Amateur Astronomer [electronic resource] : How to Advance to the Next Level
Author(s): Dave Eagle
Date:2014
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
Note:The beginning astronomical observer passes through a series of stages.The initial stage is hugely exciting and gives beginners a real buzz as they discover some of the faint fuzzy objects, markings on the planets, rings aroundSaturn and the craters o n the Moon. But as novice stargazers progress, they want to know what more there is than looking at faint fuzzy blobs or indistinct planet markings. Many novices jump to the conclusion wrongly that theyneed to spend lots of money on expensive equipment to progress. From Casual Stargazer to Amateur Astronomer has been written specifically to address this group of budding amateur astronomers. Astronomy is much more than aquick sightseeing tour. Patient observers who develop their skills will start to app reciate what they are seeing, knowing exactly what to look out for on any particular night. Equally important, they will learn what not to expect tosee. This guide is for those who want to develop their observing skills beyond mere sightseeing, learning some of the techniques used to carry out enjoyable and scientifically useful observations. It will also direct readersto information to make informed choices about what can be seen and when. All beginners who are keen to develop their skills as an amate ur astronomer can profit from the advice and gain much more from their time out observing
Contents:Moving On
Awareness of the Sky
The Sun Observational warnings and what can be seen
The Moon
Interesting and unusual features through the year
The Planets Visibility of the planets depending on the ecliptic
Dwarf Planets Finding these fairly bright minor members of the Solar System
Meteors What they dont tell you
Comets An introduction to comet hunting
Man
made Objects How to observe satellites and other man
made objects
The Stars Guide to the different types of stars visible
Deep
sky Objects Some of the objects visible througho
ISBN:9781461487661
Series:eBooks
Series:SpringerLink
Series:The Patrick Moore Practical Astronomy Series, 1431-9756
Series:Physics and Astronomy (Springer-11651)
Keywords: Astronomy
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Call number:SPRINGER-2009-9780387097725:ONLINE Show nearby items on shelf
Title:Choosing and Using a New CAT [electronic resource] : Getting the Most from Your Schmidt Cassegrain or Any Catadioptric Telescope
Author(s): Rod Mollise
Date:2009
Publisher:New York, NY : Springer New York : Imprint: Springer
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Catadioptric telescopes (CATs), such as the Schmidt Cassegrains, are increasingly popular with todays amateur astronomers and are capable of showing even the novice observer thousands of beautiful deep space wonders. ModernCATs, though, have become i ncreasingly reliant on computers. This allows them to automatically point to and track celestial objects, making astronomy more accessible to more people than ever before. Unfortunately, because of thehigh-tech nature of these telescopes, selecting one an d learning how to use it is often a difficult experience for stargazers both old and new. Thats where Choosing and Using the New CAT comes in. This book guides even the mostgreenhorn astronomer past the pitfalls encountered on the path to enjoying the bea uties and mysteries of the universe. Here you will learn not just which telescope is right for you but how to set up, operate, and maintain the mostcomplex and electronics-laden CAT. There are plenty of tips for keeping the new CAT happy and working corre ctly, and there is even guidance on advanced applications, such as hooking computers to CATs and using these telescopes to takegorgeous pictures of planets and deep space objects. This book gives readers the benefit of the authors thirty-five years experi ence using and enjoying catadioptric telescopes and solving the problems that inevitably crop up. If youdream of owning a telescope or are frustrated by the telescope you already own, this is the book for you!
Note:Springer eBooks
Contents:Why a CAT?
Whats a CAT?
Inside a CAT
Which CAT?
Making Friends with a CAT
Accessorizing a CAT
CAT
Enjoying a CAT
Care and Feeding of a CAT
Computerizing a CAT
Taking Pictures with a CAT
Choosing and Using a New CAT
Keeping the Passion Alive
ISBN:9780387097725
Series:e-books
Series:SpringerLink (Online service)
Series:Patrick Moore's Practical Astronomy Series, 1431-9756
Series:Physics and Astronomy (Springer-11651)
Keywords: Astronomy
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Call number:SPRINGER-2008-9780387741017:ONLINE Show nearby items on shelf
Title:Bayesian Networks and Influence Diagrams [electronic resource] : A Guide to Construction and Analysis
Author(s): Uffe B Kjrulff
Anders L Madsen
Date:2008
Publisher:New York, NY : Springer New York
Size:1 online resource
Note:Springer e-book platform
Note:Springer 2013 e-book collections
Note:Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methodsfor diagnosis, predi ction, decision making, classification, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide forpractitioners who wish to understand, construct, a nd analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skillsor deep understanding of the underlying theory and methods nor does it d iscuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises areincluded for the reader to check his/her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, andanalyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide. Uffe B. Kjrulff holds a PhD on probabilistic networks and is an Associate Professor ofComputer Science at Aalborg University. Anders L. Madsen holds a PhD on probabilistic networks and is the CEO of HUGIN Expert A/S
Note:Springer eBooks
Contents:Introduction
Networks
Probabilities
Probabilistic Networks
Solving Probabilistic Networks
Eliciting the Model
Modeling Techniques
Data
Driven Modeling
Conflict Analysis
Sensitivity Analysis
Value of Information Analysis
ISBN:9780387741017
Series:e-books
Series:SpringerLink (Online service)
Series:Information Science and Statistics, 1613-9011
Series:Mathematics and Statistics (Springer-11649)
Keywords: Statistics , Computer science , Data mining , Artificial intelligence , Operations research , Distribution (Probability theory) , Mathematical statistics
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Call number:Q325.5.S45::2018 Show nearby items on shelf
Title:The Deep Learning Revolution
Author(s): Terrence Sejnowski
Date:2018
Publisher:MIT Press
ISBN:9780262038034
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Call number:Q325.5.P37::2017 Show nearby items on shelf
Title:Pro Deep Learning with TensorFlow
Author(s): Santanu Pattanayak
Date:2017
ISBN:9781484230954
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Call number:Q325.5.G66::2016 Show nearby items on shelf
Title:Deep learning
Author(s): Yoshua Bengio Ian Goodfellow and Aaron Courville
Date:2016
Publisher:Cambridge, Massachusetts : The MIT Press
Size:775 p
Contents:Applied math and machine learning basics. Linear algebra -- Probability and information theory -- Numerical computation -- Machine learning basics -- Deep networks: modern practices. Deep feedforward networks -- Regularization for deep learning -- O ptimization for training deep models -- Convolutional networks -- Sequence modeling: recurrent and recursive nets -- Practical methodology -- Applications -- Deep learning research. Linear factor models -- Autoencoders -- Representation learning -- Struct ured probabilistic models for deep learning -- Monte Carlo methods -- Confronting the partition function -- Approximate inference -- Deep generative models
ISBN:9780262035613
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Call number:QA76.73.P98C32::2018 Show nearby items on shelf
Title:Deep learning with Python
Author(s): François Chollet
Date:2018
Publisher:Manning Publications Co.
Size:361 p
Contents:What is deep learning? -- Before we begin: the mathematical building blocks o fneural networks -- Getting started with neural networks -- Fundamentals of machine learning -- Deep learning for computer vision -- Deep learning for text and sequences -- Advanced deep-learning best practices -- Generative deep learning -- Conclusions
ISBN:9781617294433
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Call number:QA325.5.P38::2017 Show nearby items on shelf
Title:Deep learning : a practitioner's approach
Author(s): Josh AUTHOR = Gibson Patterson Adam
Date:2017
Edition:First edition
Publisher:Sebastopol, CA : O'Reilly
Size:507 p
Contents:A review of machine learning -- Foundations of neural networks and deep learning -- Fundamentals of deep networks -- Major architectures of deep networks -- Building deep networks -- Tuning deep networks -- Tuning specific deep network architectures -- Vectorization -- Using deep learning and DL4J on spark
ISBN:9781491914250
Keywords: Machine learning , Neural networks , Open source software
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Call number:QA325.5.B837::2017 Show nearby items on shelf
Title:Fundamentals of deep learning : designing next-generation machine intelligence algorithms
Author(s): Nikhil Buduma
Date:2017
Edition:First Edition
Publisher:O'Reilly Media
Size:283 p
Contents:The neural network -- Training feed-forward neural networks -- Implementing neural networks in TensorFlow -- Beyond gradient descent -- Convolutional neural networks -- Embedding and representation learning -- Models for sequence analysis -- Memory augmented neural networks -- Deep reinforcement learning
ISBN:9781491925614
Keywords: Deep Learning , Neural networks , Machine learning , Artificial intelligence
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Call number:ILL190713545 Show nearby items on shelf
Title:Pro Deep Learning with TensorFlow
Author(s): Santanu Pattanayak
Date:2017
Publisher:Apress
ISBN:9781484230954
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