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Format: 1 Online-Ressource (ix, 284 Seiten)

Edition: Online_Ausgabe Boulder, Colo NetLibrary 2003 E-Books von NetLibrary Sonstige Standardnummer des Gesamttitels: 22382847

ISBN: 1591401399

Additional Edition: Reproduktion von E-business innovation and change management 2004

Language: English

Keywords: Electronic Commerce ; Management ; Electronic books. ; Electronic books. ; Electronic books

URL: Volltext

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Format: 1 online resource (589 pages)

ISBN: 9783319281124

Series Statement: Water Science and Technology Library v.72

Content: Intro -- Preface -- Acknowledgments -- Contents -- List of Contributors -- Editors Biography -- Part I: Introduction -- Chapter 1: Balanced Urban Development: Is It a Myth or Reality? -- 1.1 Introduction -- 1.2 What Makes a City Liveable? -- 1.3 Urbanisation is Inevitable -- 1.4 The Role of Water in Peri-Urban Landscapes -- 1.5 Key Challenges of Sustaining Future Urban Areas -- 1.5.1 Knowledge and Capacity Building Actions for Future Cities -- 1.6 The Process of Balanced Urban Development -- 1.7 Concluding Remarks -- References -- Part II: Peri-Urbansation -- Chapter 2: Re-Ruralising the Urban Edge: Lessons from Europe, USA & -- the Global South -- 2.1 Introduction -- 2.2 Four Ways to Revive Sydney's Peri-Urban Agriculture -- 2.3 Lessons from the Global South -- 2.4 Lessons from the Shrinking Cities -- 2.5 The Value of Temporary Uses -- 2.5.1 Temporary Use as a Strategy for Urban-Rural Reimagining -- 2.5.1.1 Cultural Sensitivity and Shared Vision -- 2.5.1.2 Identifying Shared Values and Needs -- 2.5.1.3 Identifying Enabling Infrastructure -- 2.5.2 Temporary Urbanism in the Peri-urban Riverlands of Western Sydney -- 2.6 Conclusion -- References -- Chapter 3: Nimbyism and Nature: Whose Backyard Is It Anyway? -- 3.1 Introduction -- 3.2 The Benefits of Bushland -- 3.3 The Disbenefit of Bushland -- 3.4 The Flying Fox -- 3.5 Ku-Ring-Gai Flying Fox Management -- 3.6 Learning from the Experience of Other Land Managers -- 3.7 Viable Management Options for the Ku-Ring-Gai Flying Fox Reserve -- 3.7.1 Council Management Actions -- 3.7.2 Encouraging the Community to Adapt -- 3.8 Discussion -- 3.9 Lessons Learnt -- References -- Chapter 4: Connecting Urban and Rural Futures Through Rural Design -- 4.1 Introduction -- 4.2 Rural Design -- 4.3 Rural Design for Urban Agriculture -- 4.4 Case Studies of Rural Design -- 4.5 Concluding Remarks -- References.

Note: Description based on publisher supplied metadata and other sources

Additional Edition: ISBN 9783319281100

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783319281100

Language: English

Keywords: Electronic books

URL: lizenzpflichtig

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UID:

Format: 1 online resource

Edition: Boulder, Colo NetLibrary Online-Ressource Reproduction

ISBN: 0585035423

Series Statement: EBSCOhost eBook Collection

Note: Access may be limited to NetLibrary affiliated libraries , Reproduction

Additional Edition: Available in another form a

Language: English

Keywords: Electronic books. ; Mysticism.

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Author information: SundarSingh 1889-1929

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Format: 1 online resource (x, 121 pages) : , illustrations.

ISBN: 9781606508862

Series Statement: Technologies for sustainable life

Content: This monograph documents the current of state-of-art in Thermo- Active Foundations (TAFs) suitable for efficiently and sustainably heat and cooling buildings. TAFs, also referred to as thermal or energy piles, offer innovative and sustainable alternatives to ground-source heat pumps as well as other conventional heating, ventilating, and air conditioning (HVAC) systems to heat and cool commercial as well as residential buildings in several regions in the world. In summary, this monograph collects the latest multi-disciplinary advances in modeling, designing, and monitoring TAFs. Ultimately, it is hoped that this monograph will provide a comprehensive reference for both researchers and professionals interested in structural and thermal performance of TAFs and their applications in developing integrated and sustainable equipment and systems for the built environment.

Note: Co-published with American Society of Mechanical Engineers. , 1. Structural performance of thermo-active foundations -- 1.1 Introduction -- 1.2 Thermo-elastic soil-structure interaction -- 1.3 Design criteria -- 1.4 Thermo-mechanical load transfer analysis -- 1.4.1 Assumptions and basic aspects of the model -- 1.4.2 Load-transfer curves -- 1.4.3 Mechanical load transfer analysis -- 1.4.4 Thermo-mechanical T-z analyses -- 1.4.5 Model evaluation: impact of temperature changes -- 1.4.6 Model evaluation: impact of boundary conditions -- 1.4.7 Model evaluation: head restraint effects -- 1.4.8 Results from thermo-active foundations -- 1.5 Final comments -- 1.6 Acknowledgments -- 1.7 References -- , 2. Thermal analysis of thermoactive foundations -- 2.1 Introduction -- 2.2 Thermal modeling of TAFs -- 2.2.1 Description of TAF thermal modeling -- 2.2.2 Experimental validation -- 2.2.3 Sensitivity analysis -- 2.2.4 Impact of thermal piles on soil temperature distribution -- 2.3 Building foundation heat transfer -- 2.4 Thermal response of TAFs -- 2.5 Energy analysis of buildings with TAF systems -- 2.5.1 Application of TAFs for office buildings -- 2.5.2 Application of TAFs to residential buildings -- 2.6 Summary and conclusions -- 2.7 References -- , 3. Full scale geothermal energy pile studies at Monash University, Melbourne, Australia -- 3.1 Introduction -- 3.2 Site ground conditions -- 3.3 Instrumentation of full-scale geothermal energy piles -- 3.3.1 Single geothermal energy pile instrumentation -- 3.3.2 Instrumentation of group of geothermal energy piles -- 3.4 Heating test for single pile case -- 3.5 Mechanical tests -- 3.6 Dual pile system -- 3.6.1 Concrete curing temperature -- 3.6.2 Strains during concrete curing -- 3.7 Conclusions -- 3.8 Acknowledgments -- 3.9 References -- About the authors.

Additional Edition: Print version: ISBN 9781606508855

Language: English

Keywords: Libros electronicos.

URL: Click here for full-text via BAU. OIS credentials required.

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Format: 1 online resource (118 pages).

Edition: 1st edition

ISBN: 9781947447851 , 9781947447868

Content: At once memoir, theory, poetic prose, and fragment, No Archive Will Restore You is a feverish meditation on the body. Departing from Antonio Gramsci's summons to compile an inventory of the historical traces left in each of us, Singh engages with both the impossibility and urgent necessity of crafting an archive of the body. Through reveries on the enduring legacies of pain, desire, sexuality, race, and identity, she asks us to sense and feel what we have been trained to disavow, to re-member the body as more than itself. Why this desire for a body archive, for an assembly of history's traces deposited in me? (I worry over how to describe it, how to frame it without sounding banal or bafflingly idiosyncratic.) The body archive is an attunement, a hopeful gathering, an act of love against the foreclosures of reason. It is a way of knowing the body-self as a becoming and unbecoming thing, of scrambling time and matter, of turning toward rather than against oneself. And vitally, it is a way of thinking-feeling the body's unbounded relation to other bodies. I begin then to compile an archive of my body, an activity that from the start feels discomfortingly intimate. Too intimate and too bewildering an undertaking, because like all other bodies mine has become so many things over time, has changed dramatically through forces both natural and social. I am also, it must be noted, a person whose body has been broken and maimed many times over-a fact that I cannot yet entirely account for.

Note: Available through punctum books. , Mode of access: World Wide Web.

Language: English

DOI: 10.21983/P3.0231.1.00

URL: FULL

UID:

Format: 1 online resource (244 pages)

Edition: 1st ed.

ISBN: 9781788623087

Content: This book gives you a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning. It presents a unique problem-solution approach to tackle various problems in training different types of neural networks while taking care of the speed and accuracy of these models

Note: Cover -- Title Page -- Copyright and Credits -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: Keras Installation -- Introduction -- Installing Keras on Ubuntu 16.04 -- Getting ready -- How to do it... -- Installing miniconda -- Installing numpy and scipy -- Installing mkl -- Installing TensorFlow -- Installing Keras -- Using the Theano backend with Keras -- Installing Keras with Jupyter Notebook in a Docker image -- Getting ready -- How to do it... -- Installing the Docker container -- Installing the Docker container with the host volume mapped -- Installing Keras on Ubuntu 16.04 with GPU enabled -- Getting ready -- How to do it... -- Installing cuda -- Installing cudnn -- Installing NVIDIA CUDA profiler tools interface development files -- Installing the TensorFlow GPU version -- Installing Keras -- Chapter 2: Working with Keras Datasets and Models -- Introduction -- CIFAR-10 dataset -- How to do it... -- CIFAR-100 dataset -- How to do it... -- Specifying the label mode -- MNIST dataset -- How to do it... -- Load data from a CSV file -- How to do it... -- Models in Keras - getting started -- Anatomy of a model -- Types of models -- Sequential models -- How to do it... -- Create a Sequential model -- Compile the model -- Train the model -- Evaluate the model -- Predict using the model -- Putting it all together -- Model inspection internals -- Model compilation internals -- Initialize the loss -- Model training -- Output of the sample -- Shared layer models -- Introduction - shared input layer -- How to do it... -- Concatenate function -- Keras functional APIs -- How to do it... -- The output of the example -- Keras functional APIs - linking the layers -- How to do it... -- Model class -- Image classification using Keras functional APIs -- How to do it , Chapter 3: Data Preprocessing, Optimization, and Visualization -- Feature standardization of image data -- Getting ready -- How to do it... -- Initializing ImageDataGenerator -- Sequence padding -- Getting ready -- How to do it... -- Pre-padding with default 0.0 padding -- Post-padding -- Padding with truncation -- Padding with a non-default value -- Model visualization -- Getting ready -- How to do it... -- Code listing -- Optimization -- Common code for samples -- Optimization with stochastic gradient descent -- Getting ready -- How to do it... -- Optimization with Adam -- Getting ready -- How to do it... -- Optimization with AdaDelta -- Getting ready -- How to do it... -- Adadelta optimizer -- Optimization with RMSProp -- Getting ready -- How to do it... -- Chapter 4: Classification Using Different Keras Layers -- Introduction -- Classification for breast cancer -- How to do it... -- Data processing -- Modeling -- Full code listing -- Classification for spam detection -- How to do it... -- Data processing -- Modeling -- Full code listing -- Chapter 5: Implementing Convolutional Neural Networks -- Introduction -- Cervical cancer classification -- Getting ready -- How to do it... -- Data processing -- Modeling -- Predictions -- Digit recognition -- Getting ready -- How to do it... -- Modeling -- Chapter 6: Generative Adversarial Networks -- Introduction -- GAN overview -- Basic GAN -- Getting ready -- How to do it... -- Building a generator -- Building a discriminator -- Initialize the GAN instance -- Training the GAN -- Output plots -- Average metrics of the GAN -- Boundary seeking GAN -- Getting ready -- How to do it... -- Generator -- Discriminator -- Initializing the BGAN class -- Boundary seeking loss -- Train the BGAN -- Output the plots -- Iteration 0 -- Iteration 10000 -- Metrics of the BGAN model -- Plotting the metrics -- DCGAN , Getting ready -- How to do it... -- Generator -- Summary of the generator -- Training the generator -- Discriminator -- Build the discriminator -- Summary of the discriminator -- Compile the discriminator -- Combined model - generator and discriminator -- Train the generator using feedback from a discriminator -- Putting it all together -- The output of the program -- Average metrics of the model -- Chapter 7: Recurrent Neural Networks -- Introduction -- The need for RNNs -- Simple RNNs for time series data -- Getting ready -- Loading the dataset -- How to do it... -- Instantiate a sequential model -- LSTM networks for time series data -- LSTM networks -- LSTM memory example -- Getting ready -- How to do it... -- Encoder -- LSTM configuration and model -- Train the model -- Full code listing -- Time series forecasting with LSTM -- Getting ready -- Load the dataset -- How to do it... -- Instantiate a sequential model -- Observation -- Sequence to sequence learning for the same length output with LSTM -- Getting ready -- How to do it... -- Training data -- Model creation -- Model fit and prediction -- Chapter 8: Natural Language Processing Using Keras Models -- Introduction -- Word embedding -- Getting ready -- How to do it... -- Without embeddings -- With embeddings -- Sentiment analysis -- Getting ready -- How to do it... -- Full code listing -- Chapter 9: Text Summarization Using Keras Models -- Introduction -- Text summarization for reviews -- How to do it... -- Data processing -- Encoder-decoder architecture -- Training -- See also -- Chapter 10: Reinforcement Learning -- Introduction -- The CartPole game with Keras -- How to do it... -- Implementing the DQN agent -- The memory and remember -- The replay function -- The act function -- Hyperparameters for the DQN -- DQN agent class -- Training the agent -- Dueling DQN to play Cartpole -- Getting ready , DQN agent -- init method -- Setting the last layer of the network -- Dueling policy -- Init code base -- BoltzmannQPolicy -- Adjustment during training -- Sequential memory -- How to do it... -- Plotting the training and testing results -- Other Books You May Enjoy -- Index

Additional Edition: Print version Dua, Rajdeep Keras Deep Learning Cookbook Birmingham : Packt Publishing, Limited,c2018 ISBN 9781788621755

Language: English

Keywords: Electronic books

URL: Full-text  ((OIS Credentials Required))