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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.
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