Kooperativer Bibliotheksverbund

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Format: 1 online resource (489 pages)

Edition: 1st ed.

ISBN: 9780415581448 , 9781136197925

Content: This volume focuses on the core themes and issues related to sustainability of Asian economic development. It enables us to understand the dynamics of this process under the emerging inequitable global economic order and international environmental regime. The articles are based on unique quantitative data

Note: Cover -- Economic and Environmental Sustainability of the Asian Region -- Copyright -- Contents -- List of Tables -- List of Figures -- List of Abbreviations -- Preface -- Sustainability of Asian Economic Development: An Introduction -- Part I Issues in Sustainability of Asian Agriculture -- 1 Globalisation and Sustainability Issues in Agriculture in Asia: A Case of India -- 2 Globalisation and Its Impact on Agriculture, Food Security and Well-being of People in Pakistan -- 3 Agreement on Agriculture and Food Prices in Bangladesh -- 4 Inclusive Growth and Sustainable Development of Agriculture: An Examination of Major Indian States -- 5 Agricultural Risk, Weather Insurance, Derivatives and Agricultural Insurance -- Part II Ecological Concerns in Theory and Practice -- 6 Interrogating Marxian, Neo-Classical and Green Perspectives on the Contradictory Implications of Third World/Asian Development for Global Sustainability -- 7 India and China's National Strategy for Climate Change and Clean Development Mechanism Practice: A Comparison -- 8 Ecological Implications of Agricultural Development in Punjab -- Part III Core Themes in Economic Development -- 9 Role of Foreign Aid in Achieving Sustainable Development in Sri Lanka -- 10 Economic Development, Foreign Direct Investment and Policy in India -- 11 Framework for Planning and Management of Sustainable Inclusive Cities -- 12 Maintaining High Growth Rate and Future Energy Demand and Supply Scenario in India -- Part IV Resource Management and Policy Alternatives -- 13 Groundwater Markets, Irrigation Efficiency and Sustainability: A Study from North India -- 14 Sustainability of the Existing and Alternative Cropping Systems in South-west Punjab -- Part V Discrimination and Socio-economic Equity in Development , 15 Wageworkers' Exposure to Discrimination and Work-related Insecurity in Punjab's Urban Unorganised Establishments: A Social Class Analysis -- 16 Who Kill Their Daughters More? Insights from Census Data of Punjab -- Part VI Peasant Distress and Sustainability of the Cotton Economy -- 17 Does GM Technology Pay Rich Dividends? Reflections from Bt Cotton Farmers in Maharashtra -- 18 Cotton Economy, Cotton Producers, and Bt Cotton Cultivation in Indian Punjab: An Assessment of Economic Impact and Sustainability Issues -- About the Editors -- Notes on Contributors -- Index

Additional Edition: Print version Gill, Sucha Singh Economic and Environmental Sustainability of the Asian Region Oxford : Taylor & Francis Group,c2010 ISBN 9780415581448

Language: English

Keywords: Electronic books

URL: FULL  ((OIS Credentials Required))

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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))