Kooperativer Bibliotheksverbund

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Format: 1 Online-Ressource (120 Seiten) , Illustrationen

ISBN: 9789535138129

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-953-51-3811-2

Language: English

Subjects: Medicine

Keywords: Stein-Leventhal-Syndrom

DOI: 10.5772/intechopen.68300

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

UID:

Format: 1 online resource (xii, 316 pages) : , illustrations

ISBN: 9781351265003 , 1351265008 , 9781351264990 , 1351264990 , 9781351264983 , 1351264982

Content: "Deep Learning is now ubiquitous with applied machine learning. All of the technology giants (e.g. Google, Microsoft, Apple, etc.) are focusing on deep learning based techniques for data analytics and artificial intelligence. This technology applies quite strongly to biometrics. This book will cover all the topics in deep learning, namely convolutional neural networks, deep belief network and stacked autoenders. The focus will be on the application of these techniques to various biometric modalities: face, iris, palmprint, and fingerprints."--Provided by publisher.

Note: Deep learning : fundamentals and beyond / Shruti Nagpal, Maneet Singh, Mayank Vatsa, and Richa Singh -- Unconstrained face identification and verification using deep convolutional features / Jun-Cheng Chen, Rajeev Ranjan, Vishal M. Patel, Carlos D. Castillo, and Rama Chellappa -- Deep Siamese convolutional neural networks for identical twins and look-alike identification / Xiaoxia Sun, Amirsina Torfi, and Nasser Nasrabadi -- Tackling the optimization and precision weakness of deep cascaded regression for facial key-point localization / Yuhang Wu, Shishir K. Shah, and Ioannis A. Kakadiaris -- Learning deep metrics for person re-identification / Hailin Shi, Shengcai Liao, Dong Yi, and Stan Z. Li -- Deep face representation learning for kinship verification / Naman Kohli, Daksha Yadav, Mayank Vatsa, Richa Singh, and Afzel Noore -- What's hiding in my deep features? / Ethan M. Rudd, Manuel Gunther, Akshay R. Dhamija, Faris A. Kateb, and Terrance E. Boult -- Stacked correlation filters / Jonathon M. Smereka, Vishnu Naresh Boddeti, and B.V.K. Vijaya Kumar -- Learning representations for unconstrained fingerprint recognition / Aakarsh Malhotra, Anush Sankaran, Mayank Vatsa, and Richa Singh -- Person identification using handwriting dynamics and convolutional neural networks / Gustavo H. Rosa, Joao P. Papa, and Walter J. Scheirer -- Counteracting presentation attacks in face, fingerprint and iris recognition / Allan Pinto, Helio Pedrini, Michael Krumdick, Benedict Becker, Adam Czajka, Kevin W. Bowyer, and Anderson Rocha -- Fingervein presentation attack detection using transferable features from deep convolution neural networks / Raghavendra Ramachandra, Kiran B. Raja, Sushma Venkatesh, and Christoph Busch.

Additional Edition: Print version: Deep learning in biometrics. Boca Raton, FL : CRC Press, [2018] ISBN 9781138578234

Language: English

Subjects: Computer Science

Keywords: Electronic books. ; Electronic books.

URL: https://www.taylorfrancis.com/books/9781351265003

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Format: 1 online resource (339 pages)

ISBN: 9781351021739 , 9781351021746

Note: Description based on publisher supplied metadata and other sources

Additional Edition: Erscheint auch als Druck-Ausgabe Bagwari, Ashish Advanced Wireless Sensing Techniques for 5G Networks Milton : CRC Press LLC,c2018 ISBN 9780815378372

Language: English

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Format: 1 Online-Ressource

ISBN: 9780815399780 , 9781351138666

Uniform Title: NanoBioMaterials

Note: Includes bibliographical references and index

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-8153-9978-0

Language: English

Subjects: Chemistry/Pharmacy

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))

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Format: 80 Blätter , Illustrationen , 1 CD-ROM

Content: Maschinenbau

Note: unbefristet gesperrt , Masterarbeit Technische Hochschule

Language: English

Keywords: Elastomer ; Prüftechnik ; Hochschulschrift

UID:

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

URL: FULL

UID:

Format: 25 Seiten , Diagramme

Series Statement: Globale Trends - Analysen 2018, 03

Content: Mit dem Bestreben, Flüchtlinge sowie Migrantinnen und Migranten von ihren Territorien fern zu halten, entziehen sich die westlichen Nationen ihrer historischen und politischen Verantwortung, so die Analyse des renommierten indischen Migrationsforschers B.S. Chimni. Und nicht nur das: indem sie es den armen und ärmsten Ländern der Welt überlassen, mit der steigenden Zahl an Flüchtlingen umzugehen, lassen sie es zu, dass neue Krisenherde entstehen. Chimni fordert deshalb eine gerechte Antwort der internationalen Gemeinschaft auf die globale Flüchtlingskrise. Diese könne nur in einer mehrgleisigen und mehrdimensionalen Strategie bestehen, die im Dialog aller Akteure und unter Berücksichtigung aller relevanten wirtschaftlichen und politischen Faktoren erarbeitet werden müsse. (AUT)

Language: German

Keywords: Sammlung von Beiträgen