Successfully build, tune, deploy, and productionize any machine learning model, and know how to automate the process from data processing to deployment. This book is divided into three parts. Part I introduces basic cloud concepts and terminologies related to AWS services such as S3, EC2, Identity Access Management, Roles, Load Balancer, and Cloud Formation. It also covers cloud security topics such as AWS Compliance and artifacts, and the AWS Shield and CloudWatch monitoring service built for developers and DevOps engineers. Part II covers machine learning in AWS using SageMaker, which gives developers and data scientists the ability to build, train, and deploy machine learning models. Part III explores other AWS services such as Amazon Comprehend (a natural language processing service that uses machine learning to find insights and relationships in text), Amazon Forecast (helps you deliver accurate forecasts), and Amazon Textract. By the end of the book, you will understand the machine learning pipeline and how to execute any machine learning model using AWS. The book will also help you prepare for the AWS Certified Machine Learning-Specialty certification exam. What You Will Learn Be familiar with the different machine learning services offered by AWS Understand S3, EC2, Identity Access Management, and Cloud Formation Understand SageMaker, Amazon Comprehend, and Amazon Forecast Execute live projects: from the pre-processing phase to deployment on AWS Who This Book Is For Machine learning engineers who want to learn AWS machine learning services, and acquire an AWS machine learning specialty certification

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You'll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You'll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you'll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. What You Will Learn Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects Who This Book Is For Data scientists and software developers interested in image processing and computer vision.

Gain insight into fuzzy logic and neural networks, and how the integration between the two models makes intelligent systems in the current world. This book simplifies the implementation of fuzzy logic and neural network concepts using Python. You'll start by walking through the basics of fuzzy sets and relations, and how each member of the set has its own membership function values. You'll also look at different architectures and models that have been developed, and how rules and reasoning have been defined to make the architectures possible. The book then provides a closer look at neural networks and related architectures, focusing on the various issues neural networks may encounter during training, and how different optimization methods can help you resolve them. In the last section of the book you'll examine the integrations of fuzzy logics and neural networks, the adaptive neuro fuzzy Inference systems, and various approximations related to the same. You'll review different types of deep neuro fuzzy classifiers, fuzzy neurons, and the adaptive learning capability of the neural networks. The book concludes by reviewing advanced neuro fuzzy models and applications. What You'll Learn Understand fuzzy logic, membership functions, fuzzy relations, and fuzzy inference Review neural networks, back propagation, and optimization Work with different architectures such as Takagi-Sugeno model, Hybrid model, genetic algorithms, and approximations Apply Python implementations of deep neuro fuzzy system Who This book Is For Data scientists and software engineers with a basic understanding of Machine Learning who want to expand into the hybrid applications of deep learning and fuzzy logic.

Cancer is a disease of autonomous and uncontrolled cell proliferation which deregulates cell division and sometimes metastasis.It is an effort to treat squamous cell carcinoma by using benefit of oncolytic property of apoptin.Which is a protein produced by chicken Infectious anemia virous in poultry.CAI is one of the smallest avian viruses it is 23-25 nm in size, icosahedral in shape and non enveloped, having a 2.3 Kb, and single-standered DNA genome.The VP3 protein "Apoptin" synthesized by CIA Virus has a unique property when exposed to human tissue cultures.Previous research has concludeded that transformed (cancerous)cells infected with a vector containing the gene for expressing VP3 (Apoptin)under go synthesis.Apoptin also causes apoptosis in SV40-transformed fibroblasts or UV-irradiated cells from individual with hereditary cancer-prone syndromes.Where as, when the gene for apoptin synthesis was expressed in infected normal cells, cell death was not observed.

Wireless Sensor Networks (WSNs) have the ability to sense their surrounding environmental conditions using battery operated sensor nodes. To ensure the nodes to work in a low power consumption mode for the long life of network, it is important that these networks are configured properly and are optimized for their usage scenario. Moreover, configuring the network should not be a difficult task since network administrator may not be familiar with WSNs specific platform. So, configuration techniques need to be studied for optimized performance of WSNs. In this book, we study two approaches by which WSNs can be configured easily - Macroprogramming and Query driven configuration of MAC protocols. The book presents simulation results which should be helpul while configuring WSNs. KeyWords: Wireless Sensor Networks, Energy-efficiency, Task-mapping, Macro-programming, Medium Access Control protocols, Query Driven Data Extraction, TinyDB, TOSSIM, TinyOS

Microstrip patch antennas are probably the most widely used type of antennas today due to their advantages such as light weight, low volume, low cost, compatibility with integrated circuits and easy to install on the rigid surface. Furthermore, they can be easily designed to operate in dual-band, multi-band application, dual or circular polarization. They are important in many commercial applications. The microstrip patch has few drawbacks such as narrow bandwidth, low efficiency, low gain. A recently introduced method that can overcome such drawbacks is by embedding the ground with a defective ground structure (DGS). The aim is to design the Microstrip rectangular patch antenna that operates at the frequencies 2.45 GHz and 5GHz.The transmission line model is used for the analysis. In designing the Microstrip patch antenna, selection of the patch width and length are the major parameters along with the feed line and Microstrip patch antenna is realized according to the requirements of the design.