Ritesh Kumar – författare

Nanotechnology Applications for Food Safety and Quality Monitoring brings together nanotechnology science-based research for food safety and quality monitoring. With the advancement in knowledge about behavior of nano-engineered materials in food and its toxicity, the application of nanotechnology is expected to reach unprecedented levels in achieving food safety. Currently, there is no practical resource of nanotechnology as a tool specifically for monitoring safety and quality. This is a practical, concise, applications-based reference that is essential for food industry researchers and scientists to monitor the safety and quality of food to ensure quality food supplies. Demonstrates how nanotechnology can improve food safety and quality Shows how nanotechnology sensors can be used for food pesticides, pathogens and microbes Discusses the benefits and risks of nanotechnology applications for food safety

Nanotechnology Applications for Food Safety and Quality Monitoring brings together nanotechnology science-based research for food safety and quality monitoring. With the advancement in knowledge about behavior of nano-engineered materials in food and its toxicity, the application of nanotechnology is expected to reach unprecedented levels in achieving food safety. Currently, there is no practical resource of nanotechnology as a tool specifically for monitoring safety and quality. This is a practical, concise, applications-based reference that is essential for food industry researchers and scientists to monitor the safety and quality of food to ensure quality food supplies.- Demonstrates how nanotechnology can improve food safety and quality- Shows how nanotechnology sensors can be used for food pesticides, pathogens and microbes- Discusses the benefits and risks of nanotechnology applications for food safety

Work through practical recipes to learn how to solve complex machine learning and deep learning problems using PythonKey FeaturesGet up and running with artificial intelligence in no time using hands-on problem-solving recipesExplore popular Python libraries and tools to build AI solutions for images, text, sounds, and imagesImplement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much moreBook DescriptionArtificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research.Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you’ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you’ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems.By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production.What you will learnImplement data preprocessing steps and optimize model hyperparametersDelve into representational learning with adversarial autoencodersUse active learning, recommenders, knowledge embedding, and SAT solversGet to grips with probabilistic modeling with TensorFlow probabilityRun object detection, text-to-speech conversion, and text and music generationApply swarm algorithms, multi-agent systems, and graph networksGo from proof of concept to production by deploying models as microservicesUnderstand how to use modern AI in practiceWho this book is forThis AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. You’ll also find this book useful if you’re looking for state-of-the-art solutions to perform different machine learning tasks in various use cases. Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book.

Work through practical recipes to learn how to solve complex machine learning and deep learning problems using Python

Key Features

Get up and running with artificial intelligence in no time using hands-on problem-solving recipesExplore popular Python libraries and tools to build AI solutions for images, text, sounds, and imagesImplement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much more

Book Description

Artificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research.

Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you’ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you''ll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you''ll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you’ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems.

By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production.

What you will learn

Implement data preprocessing steps and optimize model hyperparametersDelve into representational learning with adversarial autoencodersUse active learning, recommenders, knowledge embedding, and SAT solversGet to grips with probabilistic modeling with TensorFlow probabilityRun object detection, text-to-speech conversion, and text and music generationApply swarm algorithms, multi-agent systems, and graph networksGo from proof of concept to production by deploying models as microservicesUnderstand how to use modern AI in practice

Who this book is for

This AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. You’ll also find this book useful if you’re looking for state-of-the-art solutions to perform different machine learning tasks in various use cases. Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book.

This book focuses on the surface plasmon resonance (SPR) technique covering fibre optic sensor research. It highlights recent advancements in geometric feature-based fibre optic SPR sensors for chemical/biochemical/biosensor applications. The contents also discuss the principle of the SPR sensing technique as well as various designs of fibre optic SPR probes for improving sensor sensitivity. It also includes numerous examples of SPR-based fibre optic sensors with various geometric (such as U-type, taper type, D-type, and interferometric-based) sensors. This volume will be a useful reference to those in academia and industry especially researchers with useful information focusing on fibre optic SPR sensors.

This book focuses on the surface plasmon resonance (SPR) technique covering fibre optic sensor research. It highlights recent advancements in geometric feature-based fibre optic SPR sensors for chemical/biochemical/biosensor applications. The contents also discuss the principle of the SPR sensing technique as well as various designs of fibre optic SPR probes for improving sensor sensitivity. It also includes numerous examples of SPR-based fibre optic sensors with various geometric (such as U-type, taper type, D-type, and interferometric-based) sensors. This volume will be a useful reference to those in academia and industry especially researchers with useful information focusing on fibre optic SPR sensors.