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In this anthology, Vietnamese writers describe their experience of
what they call the American War and its lasting legacy through the
lens of their own vital artistic visions. A North Vietnamese
soldier forms a bond with an abandoned puppy. Cousins find their
lives upended by the revelation that their fathers fought on
opposite sides of the war. Two lonely veterans in Hanoi meet years
after the war has ended through a newspaper dating service. A
psychic assists the search for the body of a long-vanished soldier.
The father of a girl suffering from dioxin poisoning struggles with
corrupt local officials. The twenty short stories collected in
Other Moons range from the intensely personal to narratives that
deal with larger questions of remembrance, trauma, and healing. By
a diverse set of authors, including many veterans, they span styles
from social realism to tales of the fantastic. Yet whether
describing the effects of Agent Orange exposure or telling ghost
stories, all speak to the unresolved legacy of a conflict that
still haunts Vietnam. Among the most widely anthologized and
popular pieces of short fiction about the war in Vietnam, these
works appear here for the first time in English. Other Moons offers
Anglophone audiences an unparalleled opportunity to experience how
the Vietnamese think and write about the conflict that consumed
their country from 1954 to 1975-a perspective still largely missing
from American narratives.
In this anthology, Vietnamese writers describe their experience of
what they call the American War and its lasting legacy through the
lens of their own vital artistic visions. A North Vietnamese
soldier forms a bond with an abandoned puppy. Cousins find their
lives upended by the revelation that their fathers fought on
opposite sides of the war. Two lonely veterans in Hanoi meet years
after the war has ended through a newspaper dating service. A
psychic assists the search for the body of a long-vanished soldier.
The father of a girl suffering from dioxin poisoning struggles with
corrupt local officials. The twenty short stories collected in
Other Moons range from the intensely personal to narratives that
deal with larger questions of remembrance, trauma, and healing. By
a diverse set of authors, including many veterans, they span styles
from social realism to tales of the fantastic. Yet whether
describing the effects of Agent Orange exposure or telling ghost
stories, all speak to the unresolved legacy of a conflict that
still haunts Vietnam. Among the most widely anthologized and
popular pieces of short fiction about the war in Vietnam, these
works appear here for the first time in English. Other Moons offers
Anglophone audiences an unparalleled opportunity to experience how
the Vietnamese think and write about the conflict that consumed
their country from 1954 to 1975-a perspective still largely missing
from American narratives.
Fun and exciting projects to learn what artificial minds can create
Key Features Code examples are in TensorFlow 2, which make it easy
for PyTorch users to follow along Look inside the most famous deep
generative models, from GPT to MuseGAN Learn to build and adapt
your own models in TensorFlow 2.x Explore exciting, cutting-edge
use cases for deep generative AI Book DescriptionMachines are
excelling at creative human skills such as painting, writing, and
composing music. Could you be more creative than generative AI? In
this book, you'll explore the evolution of generative models, from
restricted Boltzmann machines and deep belief networks to VAEs and
GANs. You'll learn how to implement models yourself in TensorFlow
and get to grips with the latest research on deep neural networks.
There's been an explosion in potential use cases for generative
models. You'll look at Open AI's news generator, deepfakes, and
training deep learning agents to navigate a simulated environment.
Recreate the code that's under the hood and uncover surprising
links between text, image, and music generation. What you will
learn Export the code from GitHub into Google Colab to see how
everything works for yourself Compose music using LSTM models,
simple GANs, and MuseGAN Create deepfakes using facial landmarks,
autoencoders, and pix2pix GAN Learn how attention and transformers
have changed NLP Build several text generation pipelines based on
LSTMs, BERT, and GPT-2 Implement paired and unpaired style transfer
with networks like StyleGAN Discover emerging applications of
generative AI like folding proteins and creating videos from images
Who this book is forThis is a book for Python programmers who are
keen to create and have some fun using generative models. To make
the most out of this book, you should have a basic familiarity with
math and statistics for machine learning.
Gain practical insights by exploiting data in your business to
build advanced predictive modeling applications Key Features A
step-by-step guide to predictive modeling including lots of tips,
tricks, and best practices Learn how to use popular predictive
modeling algorithms such as Linear Regression, Decision Trees,
Logistic Regression, and Clustering Master open source Python tools
to build sophisticated predictive models Book Description Social
Media and the Internet of Things have resulted in an avalanche of
data. Data is powerful but not in its raw form; it needs to be
processed and modeled, and Python is one of the most robust tools
out there to do so. It has an array of packages for predictive
modeling and a suite of IDEs to choose from. Using the Python
programming language, analysts can use these sophisticated methods
to build scalable analytic applications. This book is your guide to
getting started with predictive analytics using Python. You'll
balance both statistical and mathematical concepts, and implement
them in Python using libraries such as pandas, scikit-learn, and
NumPy. Through case studies and code examples using popular
open-source Python libraries, this book illustrates the complete
development process for analytic applications. Covering a wide
range of algorithms for classification, regression, clustering, as
well as cutting-edge techniques such as deep learning, this book
illustrates explains how these methods work. You will learn to
choose the right approach for your problem and how to develop
engaging visualizations to bring to life the insights of predictive
modeling. Finally, you will learn best practices in predictive
modeling, as well as the different applications of predictive
modeling in the modern world. The course provides you with highly
practical content from the following Packt books: 1. Learning
Predictive Analytics with Python 2. Mastering Predictive Analytics
with Python What you will learn Understand the statistical and
mathematical concepts behind predictive analytics algorithms and
implement them using Python libraries Get to know various methods
for importing, cleaning, sub-setting, merging, joining,
concatenating, exploring, grouping, and plotting data with pandas
and NumPy Master the use of Python notebooks for exploratory data
analysis and rapid prototyping Get to grips with applying
regression, classification, clustering, and deep learning
algorithms Discover advanced methods to analyze structured and
unstructured data Visualize the performance of models and the
insights they produce Ensure the robustness of your analytic
applications by mastering the best practices of predictive analysis
Who this book is for This book is designed for business analysts,
BI analysts, data scientists, or junior level data analysts who are
ready to move on from a conceptual understanding of advanced
analytics and become an expert in designing and building advanced
analytics solutions using Python. If you are familiar with coding
in Python (or some other programming/statistical/scripting
language) but have never used or read about predictive analytics
algorithms, this book will also help you.
Exploit the power of data in your business by building advanced
predictive modeling applications with Python About This Book *
Master open source Python tools to build sophisticated predictive
models * Learn to identify the right machine learning algorithm for
your problem with this forward-thinking guide * Grasp the major
methods of predictive modeling and move beyond the basics to a
deeper level of understanding Who This Book Is For This book is
designed for business analysts, BI analysts, data scientists, or
junior level data analysts who are ready to move from a conceptual
understanding of advanced analytics to an expert in designing and
building advanced analytics solutions using Python. You're expected
to have basic development experience with Python. What You Will
Learn * Gain an insight into components and design decisions for an
analytical application * Master the use Python notebooks for
exploratory data analysis and rapid prototyping * Get to grips with
applying regression, classification, clustering, and deep learning
algorithms * Discover the advanced methods to analyze structured
and unstructured data * Find out how to deploy a machine learning
model in a production environment * Visualize the performance of
models and the insights they produce * Scale your solutions as your
data grows using Python * Ensure the robustness of your analytic
applications by mastering the best practices of predictive analysis
In Detail The volume, diversity, and speed of data available has
never been greater. Powerful machine learning methods can unlock
the value in this information by finding complex relationships and
unanticipated trends. Using the Python programming language,
analysts can use these sophisticated methods to build scalable
analytic applications to deliver insights that are of tremendous
value to their organizations. In Mastering Predictive Analytics
with Python, you will learn the process of turning raw data into
powerful insights. Through case studies and code examples using
popular open-source Python libraries, this book illustrates the
complete development process for analytic applications and how to
quickly apply these methods to your own data to create robust and
scalable prediction services. Covering a wide range of algorithms
for classification, regression, clustering, as well as cutting-edge
techniques such as deep learning, this book illustrates not only
how these methods work, but how to implement them in practice. You
will learn to choose the right approach for your problem and how to
develop engaging visualizations to bring the insights of predictive
modeling to life Style and approach This book emphasizes on
explaining methods through example data and code, showing you
templates that you can quickly adapt to your own use cases. It
focuses on both a practical application of sophisticated algorithms
and the intuitive understanding necessary to apply the correct
method to the problem at hand. Through visual examples, it also
demonstrates how to convey insights through insightful charts and
reporting.
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