Ankit Jain – författare

Tumour-Targeting with Stimuli Responsive Vesicular Nanocarriers: Basics to Emerging Applications not only acquaints scientists and science interns with the fundamental knowledge of the experimental aspects and state-of-the-art technology for vesicular carriers to equip their rational application in tumor targeting, but also provides a holistic understanding of regulatory concerns, patents, and clinical trials. In 15 chapters. this book provides basics and advances in stimuli-responsive vesicular carriers for tumor targeting based on findings outlined in highly organized tables, illustrative figures, and flow charts.With a focus on the extrapolation of bioengineering tools in the domain of vesicular carriers, stimuli-responsive features concerning tumors, drug delivery approaches, and associated challenges, it helps readers to define the major gaps in knowledge that can lead to significant scientific discoveries in therapy, diagnosis, and theragnosis of cancer.Addresses the unmet needs and challenges confronted by nanotechnologists to foster collaboration of biologists, chemists, physicists, bioengineers, and cliniciansProvides an explicit account of vital aspects of PEGylation of these carriers and challenges for effective tumor targetingPresents a need-of-hour discussion over regulatory concerns, clinical trials, and patents

Tumour-Targeting with Stimuli Responsive Vesicular Nanocarriers: Basics to Emerging Applications not only acquaints scientists and science interns with the fundamental knowledge of the experimental aspects and state-of-the-art technology for vesicular carriers to equip their rational application in tumor targeting, but also provides a holistic understanding of regulatory concerns, patents, and clinical trials. In 15 chapters. this book provides basics and advances in stimuli-responsive vesicular carriers for tumor targeting based on findings outlined in highly organized tables, illustrative figures, and flow charts.With a focus on the extrapolation of bioengineering tools in the domain of vesicular carriers, stimuli-responsive features concerning tumors, drug delivery approaches, and associated challenges, it helps readers to define the major gaps in knowledge that can lead to significant scientific discoveries in therapy, diagnosis, and theragnosis of cancer.- Addresses the unmet needs and challenges confronted by nanotechnologists to foster collaboration of biologists, chemists, physicists, bioengineers, and clinicians- Provides an explicit account of vital aspects of PEGylation of these carriers and challenges for effective tumor targeting- Presents a need-of-hour discussion over regulatory concerns, clinical trials, and patents

Master the intricacies of Apache Storm and develop real-time stream processing applications with easeAbout This Book• Exploit the various real-time processing functionalities offered by Apache Storm such as parallelism, data partitioning, and more• Integrate Storm with other Big Data technologies like Hadoop, HBase, and Apache Kafka• An easy-to-understand guide to effortlessly create distributed applications with StormWho This Book Is ForIf you are a Java developer who wants to enter into the world of real-time stream processing applications using Apache Storm, then this book is for you. No previous experience in Storm is required as this book starts from the basics. After finishing this book, you will be able to develop not-so-complex Storm applications. What You Will Learn• Understand the core concepts of Apache Storm and real-time processing• Follow the steps to deploy multiple nodes of Storm Cluster• Create Trident topologies to support various message-processing semantics• Make your cluster sharing effective using Storm scheduling• Integrate Apache Storm with other Big Data technologies such as Hadoop, HBase, Kafka, and more• Monitor the health of your Storm clusterIn DetailApache Storm is a real-time Big Data processing framework that processes large amounts of data reliably, guaranteeing that every message will be processed. Storm allows you to scale your data as it grows, making it an excellent platform to solve your big data problems. This extensive guide will help you understand right from the basics to the advanced topics of Storm.The book begins with a detailed introduction to real-time processing and where Storm fits in to solve these problems. You'll get an understanding of deploying Storm on clusters by writing a basic Storm Hello World example. Next we'll introduce you to Trident and you'll get a clear understanding of how you can develop and deploy a trident topology. We cover topics such as monitoring, Storm Parallelism, scheduler and log processing, in a very easy to understand manner. You will also learn how to integrate Storm with other well-known Big Data technologies such as HBase, Redis, Kafka, and Hadoop to realize the full potential of Storm.With real-world examples and clear explanations, this book will ensure you will have a thorough mastery of Apache Storm. You will be able to use this knowledge to develop efficient, distributed real-time applications to cater to your business needs.Style and approachThis easy-to-follow guide is full of examples and real-world applications to help you get an in-depth understanding of Apache Storm. This book covers the basics thoroughly and also delves into the intermediate and slightly advanced concepts of application development with Apache Storm.

Implement TensorFlow\'s offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projectsKey FeaturesUse machine learning and deep learning principles to build real-world projectsGet to grips with TensorFlow's impressive range of module offeringsImplement projects on GANs, reinforcement learning, and capsule networkBook DescriptionTensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem.To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification.As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts.By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work.What you will learnUnderstand the TensorFlow ecosystem using various datasets and techniquesCreate recommendation systems for quality product recommendationsBuild projects using CNNs, NLP, and Bayesian neural networksPlay Pac-Man using deep reinforcement learningDeploy scalable TensorFlow-based machine learning systemsGenerate your own book script using RNNsWho this book is forTensorFlow Machine Learning Projects is for you if you are a data analyst, data scientist, machine learning professional, or deep learning enthusiast with basic knowledge of TensorFlow. This book is also for you if you want to build end-to-end projects in the machine learning domain using supervised, unsupervised, and reinforcement learning techniques

Implement TensorFlow''s offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projectsKey FeaturesUse machine learning and deep learning principles to build real-world projectsGet to grips with TensorFlow''s impressive range of module offeringsImplement projects on GANs, reinforcement learning, and capsule networkBook DescriptionTensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits-simplicity, efficiency, and flexibility-of using TensorFlow in various real-world projects. With the help of this book, you''ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem.To start with, you''ll get to grips with using TensorFlow for machine learning projects; you''ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification.As you make your way through the book, you''ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You''ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts.By the end of this book, you''ll have gained the required expertise to build full-fledged machine learning projects at work.What you will learnUnderstand the TensorFlow ecosystem using various datasets and techniquesCreate recommendation systems for quality product recommendationsBuild projects using CNNs, NLP, and Bayesian neural networksPlay Pac-Man using deep reinforcement learningDeploy scalable TensorFlow-based machine learning systemsGenerate your own book script using RNNsWho this book is forTensorFlow Machine Learning Projects is for you if you are a data analyst, data scientist, machine learning professional, or deep learning enthusiast with basic knowledge of TensorFlow. This book is also for you if you want to build end-to-end projects in the machine learning domain using supervised, unsupervised, and reinforcement learning techniques