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Umfang: 1 online resource (518 pages)

Ausgabe: 1st ed.

ISBN: 9780195077360 , 9780195358919

Anmerkung: Intro -- Contents -- Contributors -- 1. Organizational Hierarchies and Evolutionary Processes: Some Reflections on a Theory of Organizational Evolution -- Part I: Introductory Essays -- 2. How Individual and Face-to-Face-Group Selection Undermine Firm Selection in Organizational Evolution -- 3. The Evolution of Evolution -- Part II: Intraorganizational Evolution -- 4. An Intraorganizational Ecological Perspective on Managerial Risk Behavior, Performance, and Survival: Individual, Organizational, and Environmental Effects -- 5. Seeking Adaptive Advantage: Evolutionary Theory and Managerial Action -- 6. Organizing for Continuous Improvement: Evolutionary Theory Meets the Quality Revolution -- COMMENTARIES -- Turning Evolution Inside the Organization -- Evolution, Externalities and Managerial Action -- Part III: Organizational Evolution -- 7. Evolutionary Processes and Patterns of Core Business Change -- 8. The Ecological Dynamics of Organizational Change: Density and Mass Dependence in Rates of Entry into New Markets -- 9. Surviving Schumpeterian Environments: An Evolutionary Perspective -- 10. Mimetic Learning and the Evolution of Organizational Populations -- COMMENTARIES -- Taking on Strategy, 1-2-3 -- On Behalf of Naïveté -- Part IV: Population Evolution -- 11. Minimalism, Mutalism, and Maturity: The Evolution of the American Trade Association Population in the 20th Century -- 12. Disruptive Selection and Population Segmentation: Interpopulation Competition as a Segregation Process -- 13. Externalities and Ecological Theory: Unbundling Density Dependence -- 14. Resource Partitioning and Foundings of Banking Cooperatives in Italy -- 15. The Evolution of Socially Contingent Rational Action: Effects of Labor Strikes on Change in Union Founding in the 1880s -- COMMENTARIES -- Evolution and Organizational Science , Progress and Problems in Population Ecology -- Part V: Community Evolution -- 16. The Liability of Collective Action: Growth and Change Among Early American Telephone Companies -- 17. Density-Independent Selection and Community Evolution -- 18. Organization-Environment Coevolution -- 19. The Coevolution of Technology and Organization -- 20. The Coevolution of Technical and Institutional Events in the Development of an Innovation -- COMMENTARIES -- The Challenge of Community Evolution -- On the Concept of "Organizational Community -- References -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- Y -- Z.

Weitere Ausg.: Print version Baum, Joel A. C. Evolutionary Dynamics of Organizations Oxford : Oxford University Press, Incorporated,c1994 ISBN 9780195077360

Sprache: Englisch

Schlagwort(e): Electronic books

URL: FULL  ((OIS Credentials Required))

UID:

Umfang: 1 online resource (244 pages)

Ausgabe: 1st ed.

ISBN: 9781788623087

Inhalt: 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

Anmerkung: 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

Weitere Ausg.: Print version Dua, Rajdeep Keras Deep Learning Cookbook Birmingham : Packt Publishing, Limited,c2018 ISBN 9781788621755

Sprache: Englisch

Schlagwort(e): Electronic books

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