Dipayan Sarkar – författare

A two volume set that kick off the Food Biotechnology series, Functional Foods and Biotechnology combines the work of experts around that world to advance knowledge on bio-based innovations to improve wider health-focused applications of functional food and food ingredients. Volume I focuses on the recent advances in the understanding of the role of cellular, metabolic, and biochemical concepts and processing that are important and relevant to improve functional foods and food ingredients targeting human health benefits. Volume II highlights two important and interrelated themes: biotransformation innovations and novel bio-based analytical tools for understanding and advancing functional foods and food ingredients for health-focused food and nutritional security solutionsKey Features: Provides ecological and metabolic rational to integrate novel functional food and functional ingredient sources in wider health-focused food system innovations. Examines the value-added role of select functional foods and food ingredients to improve NCD-linked health benefits such as type-2 diabetes, cardiovascular disease, and human gut improvement Provides system science-based food biotechnology innovations to design and advance functional foods and food ingredients for solutions to emerging global food and nutritional insecurity coupled public health challenges. Discusses biotransformation innovations to improve human health relevant nutritional qualities of functional foods and food ingredients.The overall goal of Volume I is to provide insights on role of these functional food and ingredient sources for their integration in wider health-focused food systems, which will help food scientists, food industry personnel, nutritionists, crop science researchers, public health professionals, and policy makers to make appropriate decisions and to formulate strategies for improving health and well-being. Volume II aims to advance the knowledge on metabolically-driven food system innovations that can be targeted to enhance human health and food safety relevant nutritional qualities and antimicrobial properties of functional food and food ingredients.

The second book of the Food Biotechnology series, Functional Foods and Biotechnology: Biotransformation and Analysis of Functional Foods and Ingredients highlights two important and interrelated themes: biotransformation innovations and novel bio-based analytical tools for understanding and advancing functional foods and food ingredients for health-focused food and nutritional security solutions. The first section of this book provides novel examples of innovative biotransformation strategies based on ecological, biochemical, and metabolic rationale to target the improvement of human health relevant benefits of functional foods and food ingredients. The second section of the book focuses on novel host response based analytical tools and screening strategies to investigate and validate the human health and food safety relevant benefits of functional foods and food ingredients. Food biotechnology experts from around the world have contributed to this book to advance knowledge on bio-based innovations to improve wider health-focused applications of functional food and food ingredients, especially targeting non-communicable chronic disease (NCD) and food safety relevant solution strategies. Key Features: Provides system science-based food biotechnology innovations to design and advance functional foods and food ingredients for solutions to emerging global food and nutritional insecurity coupled public health challenges. Discusses biotransformation innovations to improve human health relevant nutritional qualities of functional foods and food ingredients. Includes novel host response-based food analytical models to optimize and improve wider health-focused application of functional foods and food ingredients.The overarching theme of this second book is to advance the knowledge on metabolically-driven food system innovations that can be targeted to enhance human health and food safety relevant nutritional qualities and antimicrobial properties of functional food and food ingredients. The examples of biotransformation innovations and food analytical models provide critical insights on current advances in food biotechnology to target, design and improve functional food and food ingredients with specific human health benefits. Such improved understanding will help to design more ecologically and metabolically relevant functional food and food ingredients across diverse global communities. The thematic structure of this second book is built from the related initial book, which is also available in the Food Biotechnology SeriesFunctional Foods and Biotechnology: Sources of Functional Food and Ingredients, edited by Kalidas Shetty and Dipayan Sarkar (ISBN: 9780367435226)For a complete list of books in this series, please visit our website at:https://www.crcpress.com/Food-Biotechnology-Series/book-series/CRCFOOBIOTECH

The first of two related books that kick off the Food Biotechnology series, Functional Foods and Biotechnology: Sources of Functional Foods and Ingredients, focuses on the recent advances in the understanding of the role of cellular, metabolic, and biochemical concepts and processing that are important and relevant to improve functional foods and food ingredients targeting human health benefits. This volume explores sources of ecologically-based diversity of functional foods and food ingredients that are available to enhance diverse nutritional values and functional benefits of foods for better human health outcomes, especially focusing on emerging diet and lifestyle-linked non-communicable chronic disease (NCDs) challenges. The contributors with expertise in the field of Food Biotechnology and Functional Food Ingredients have integrated the recent advances in some common as well as novel sources of functional foods and ingredients from diverse ecological and cultural origins. Further, these chapters also highlight human health relevant bioactive profiles and associated functionalities of these health-promoting compounds, including preventative functional roles for common NCD-linked health benefits. FEATURES: Provides ecological and metabolic rational to integrate novel functional food and functional ingredient sources in wider health-focused food system innovations. Examines the value-added role of select functional foods and food ingredients to improve NCD-linked health benefits such as type-2 diabetes, cardiovascular disease, and human gut improvement Includes insights on system-based solutions to advance climate resilient and health focused food diversity based on diverse biotechnological approaches to design and integrate functional food and food ingredient sourcesOverall, the rationale of this book series is focused on Metabolic-Driven Rationale to Advance Biotechnological Approaches for Functional Foods, the synopsis of which is presented as the Introduction chapter, which is followed by a chapter on current understanding about regulatory guidelines for health claims of functional foods and food ingredients. Special topics on nonnutritive sweeteners, caroteneprotein from seafood waste, and Xylooligosaccharides as functional food ingredients for health-focused dietary applications are integrated in this book. Additionally, ecologically and metabolically-driven functional roles of common food sources such as corn, and barley and some novel food sources, such as ancient emmer wheat, black soybean, fava bean, herbs from Lamiaceae and functional protein ingredients and minerals from Lemnaceae are also highlighted in this volume. The overall goal is to provide insights on role of these functional food and ingredient sources for their integration in wider health-focused food systems, which will help food scientists, food industry personnel, nutritionists, crop science researchers, public health professionals, and policy makers to make appropriate decisions and to formulate strategies for improving health and well-being. A related book focuses on biological and metabolically driven mobilization of functional bioactives and ingredients and their analysis that is relevant in health and wellness.

Implement machine learning algorithms to build ensemble models using Keras, H2O, Scikit-Learn, Pandas and more Key FeaturesApply popular machine learning algorithms using a recipe-based approachImplement boosting, bagging, and stacking ensemble methods to improve machine learning modelsDiscover real-world ensemble applications and encounter complex challenges in Kaggle competitionsBook DescriptionEnsemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking.The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you’ll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The book will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You’ll also be able to implement models such as fraud detection, text categorization, and sentiment analysis.By the end of this book, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes.What you will learnUnderstand how to use machine learning algorithms for regression and classification problemsImplement ensemble techniques such as averaging, weighted averaging, and max-votingGet to grips with advanced ensemble methods, such as bootstrapping, bagging, and stackingUse Random Forest for tasks such as classification and regressionImplement an ensemble of homogeneous and heterogeneous machine learning algorithmsLearn and implement various boosting techniques, such as AdaBoost, Gradient Boosting Machine, and XGBoostWho this book is forThis book is designed for data scientists, machine learning developers, and deep learning enthusiasts who want to delve into machine learning algorithms to build powerful ensemble models. Working knowledge of Python programming and basic statistics is a must to help you grasp the concepts in the book.

Tackle the complex challenges faced while building end-to-end deep learning models using modern R librariesKey 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 DescriptionDeep 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 forThis 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.