Tackle the complex challenges faced while building end-to-end deep learning models using modern R libraries Key FeaturesUnderstand the intricacies of R deep learning packages to perform a range of deep learning tasksImplement deep learning techniques and algorithms for real-world use casesExplore various state-of-the-art techniques for fine-tuning neural network modelsBook Description Deep learning (DL) has evolved in recent years with developments such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning. This book will get you up and running with R 3.5.x to help you implement DL techniques. The book starts with the various DL techniques that you can implement in your apps. A unique set of recipes will help you solve binomial and multinomial classification problems, and perform regression and hyperparameter optimization. To help you gain hands-on experience of concepts, the book features recipes for implementing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Long short-term memory (LSTMs) networks, as well as sequence-to-sequence models and reinforcement learning. You’ll then learn about high-performance computation using GPUs, along with learning about parallel computation capabilities in R. Later, you’ll explore libraries, such as MXNet, that are designed for GPU computing and state-of-the-art DL. Finally, you’ll discover how to solve different problems in NLP, object detection, and action identification, before understanding how to use pre-trained models in DL apps. By the end of this book, you’ll have comprehensive knowledge of DL and DL packages, and be able to develop effective solutions for different DL problems. What you will learnWork with different datasets for image classification using CNNsApply transfer learning to solve complex computer vision problemsUse RNNs and their variants such as LSTMs and Gated Recurrent Units (GRUs) for sequence data generation and classificationImplement autoencoders for DL tasks such as dimensionality reduction, denoising, and image colorizationBuild deep generative models to create photorealistic images using GANs and VAEsUse MXNet to accelerate the training of DL models through distributed computingWho this book is for This deep learning book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to learn key tasks in deep learning domains using a recipe-based approach. A strong understanding of machine learning and working knowledge of the R programming language is mandatory.
Tackle the complex challenges faced while building end-to-end deep learning models using modern R libraries Key FeaturesUnderstand the intricacies of R deep learning packages to perform a range of deep learning tasksImplement deep learning techniques and algorithms for real-world use casesExplore various state-of-the-art techniques for fine-tuning neural network modelsBook Description Deep learning (DL) has evolved in recent years with developments such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning. This book will get you up and running with R 3.5.x to help you implement DL techniques. The book starts with the various DL techniques that you can implement in your apps. A unique set of recipes will help you solve binomial and multinomial classification problems, and perform regression and hyperparameter optimization. To help you gain hands-on experience of concepts, the book features recipes for implementing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Long short-term memory (LSTMs) networks, as well as sequence-to-sequence models and reinforcement learning. You’ll then learn about high-performance computation using GPUs, along with learning about parallel computation capabilities in R. Later, you’ll explore libraries, such as MXNet, that are designed for GPU computing and state-of-the-art DL. Finally, you’ll discover how to solve different problems in NLP, object detection, and action identification, before understanding how to use pre-trained models in DL apps. By the end of this book, you’ll have comprehensive knowledge of DL and DL packages, and be able to develop effective solutions for different DL problems. What you will learnWork with different datasets for image classification using CNNsApply transfer learning to solve complex computer vision problemsUse RNNs and their variants such as LSTMs and Gated Recurrent Units (GRUs) for sequence data generation and classificationImplement autoencoders for DL tasks such as dimensionality reduction, denoising, and image colorizationBuild deep generative models to create photorealistic images using GANs and VAEsUse MXNet to accelerate the training of DL models through distributed computingWho this book is for This deep learning book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to learn key tasks in deep learning domains using a recipe-based approach. A strong understanding of machine learning and working knowledge of the R programming language is mandatory.
What makes a national community out of a state? Addressing this fundamental question. Rajagopalan studies national integration from the perspective of three South Asian communities - Tamilians in India, Sindhis in Pakistan, and Tamils in Sri Lanka - that have a history of secessionism in common, but with vastly different outcomes Rajagopalan investigates why integration is relatively successful in some cases (Tamil Nadu), less so in others (Sindh), and disastrous in some (Sri Lanka). Broadly comparative and drawing together multiple aspects of political development and nation building, her imaginative exploration of the tension between state and nation gives voice to relatively disenfranchised sections of society.
This book presents state of art research in speech emotion recognition. Readers are first presented with basic research and applications – gradually more advance information is provided, giving readers comprehensive guidance for classify emotions through speech. Simulated databases are used and results extensively compared, with the features and the algorithms implemented using MATLAB. Various emotion recognition models like Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Support Vector Machines (SVM) and K-Nearest neighbor (KNN) and are explored in detail using prosody and spectral features, and feature fusion techniques.
Section 1: Respiratory System 1. Current Approach to Weaning: Role of Ultrasound and Biomarkers 2. Ventilation-induced Lung Injury: Ergotrauma 3. Transpulmonary Pressure: Physiology and Implications at Bedside 4. Nebulized Drug Delivery: A Contemporary Review Section 2: Hemodynamics 5. Noninvasive Hemodynamic Monitoring as a Tool: Current Practice and Evidence 6. The Choice of Vasopressor in Shock: Current Evidence 7. Best Papers of the Decade on Shock and Hemodynamic Monitoring 8. Personalized Hemodynamic Targets: The Need of the Hour? Section 3: Infections and Antimicrobials 9. Looking Beyond Antimicrobials: Newer Concepts and Technologies 10. Emerging Choices for Resistant Infections 11. Biomarkers in Sepsis: What to Expect in Future? 12. Critical Appraisal of Surviving Sepsis Guidelines 2021 Section 4: Hematology 13. Hemophagocytic Lymphohistiocytosis Syndrome: Increasing Relevance to the Intensivist and All about it 14. Expanding Role of Rotational Thromboelastometry in Critical Care: What to Expect 15. Current Evidence on Transfusion Strategies in ICU 16. Update on Neutropenic Sepsis Section 5: Organizational Issues 17. Lessons Learned: Role of Critical Care Professionals Post COVID-19 Pandemic 18. Big Data Analysis and AI: How can Intensive Care Benefit? 19. Post-ICU Follow-up: What to Look for and How to Schedule? 20. Communication in ICU Using IT Tools
This book provides both an erudite and intimate look at how Buddhism is lived in Sri Lanka. While India is known as the birthplace of Buddhism, Sri Lanka is its other home; Buddhism extends back over twenty-five hundred years on the island and remains at the center of its spiritual traditions and culture. Throughout the book, author Swarna Wickremeratne incorporates a personal view, sharing stories of herself, her family, friends, and acquaintances as they "lived Buddhism" both during her Sri Lankan girlhood and during more recent times. This personal view makes the traditions come alive as Wickremeratne details Buddhist beliefs, customs, rituals and ceremonies, and folklore. She also provides a fascinating discussion of the Sangha, the institutional monkhood in Sri Lanka, including its history, codes of conduct, and evolution and resilience over time. Wickremeratne explores the recent attempts by many monks to reinvent themselves in a society characterized by secularization, globalization, and a tide of aggressive Christian evangelization.
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