Kooperativer Bibliotheksverbund

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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: 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.