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Introduces an assortment of powerful command line utilities that
can be combined to create simple, yet powerful shell scripts for
processing datasets. The code samples and scripts use the bash
shell, and typically involve small datasets so you can focus on
understanding the features of grep, sed, and awk. Companion files
with code are available for downloading from the publisher.
This book is intended primarily for those who plan to become data
scientists as well as anyone who needs to perform data cleaning
tasks. It contains a variety of features of NumPy and Pandas and
how to create databases and tables in MySQL. Chapter 7 covers many
data wrangling tasks using Python scripts and awk-based shell
scripts. Companion files with code are available for downloading
from the publisher. FEATURES: Provides the reader with basic Python
3, Java, and Pandas programming concepts, and an introduction to
awk. Includes a chapter on RDBMs and SQL. Companion files with
code.
As part of the best-selling Pocket Primer series, this book is
designed to present the fundamentals of data structures using
Python. Data structures provide a means to managing huge amounts of
information such as large databases and the ability to use search
and sort algorithms effectively. It is intended to be a fast-paced
introduction to the core concepts of Python and data structures,
illustrated with numerous code samples. Companion files with source
code are available for downloading.
This book is for developers who are looking for an overview of
basic concepts in Natural Language Processing using R. It casts a
wide net of techniques to help developers who have a range of
technical backgrounds. Numerous code samples and listings are
included to support myriad topics. The final chapter presents the
Transformer Architecture, BERT-based models, and the GPT family of
models, all of which were developed during the past three years.
Companion files with source code and figures are included. Features
Covers extensive topics related to natural language processing
using R Features companion files with source code and figures from
the book
Houston, we have a problem... Flash cannot run on iPhone. Good
news, though...Flash can and will run on Android Android will give
you what you need to continue building cool and fresh Flash-based
Apps for mobile devices, smartphones, and more. Pro Android Flash
is the definitive guide to building Flash and other rich Internet
apps (RIAs) on the Android platform. It covers the most popular RIA
frameworks for Android developers - Flex and JavaFX - and shows you
how to build rich, immersive user experiences on both Android
smartphones and tablets. You'll learn how to incorporate
multimedia, animation, and special effects into your apps for
maximum visual appeal. You'll also cover advanced topics, including
input methods, hardware inputs, deployment, and performance
optimization.What you'll learn * Deployment of Flash, Flex, and
JavaFX to Android * How to take your desktop RIAs and adapt them
for mobile devices * How to integrate hardware inputs from the
camera, GPS, compass, and accelerometer * How to build an immersive
user interface with audio and video assets * How to integrate
styling and artifact s from a professional designer * Best
practices for mobile performance tuning and optimization Who this
book is for This book is intended for developers who are looking to
build rich Internet applications (RIAs) for the Android platform,
especially Flash, Flex, and JavaFX.
This book is intended for those who plan to become data scientists
as well as anyone who needs to perform data cleaning tasks using
Pandas and NumPy. It contains a variety of code samples and
features of NumPy and Pandas, and how to write regular expressions.
Chapter 3 includes fundamental statistical concepts and Chapter 7
covers data visualization with Matplotlib and Seaborn. Companion
files with code are available for downloading from the publisher.
This book is for developers who are looking for an introduction to
basic concepts in NLP and machine learning. Numerous code samples
and listings are included to support myriad topics. The first two
chapters contain introductory material for NumPy and Pandas,
followed by chapters on NLP concepts, algorithms and toolkits,
machine learning, and NLP applications. The final chapters include
examples of NLP tasks using TF2 and Keras, the Transformer
architecture, BERT-based models, and the GPT family of models. The
appendices contain introductory material (including Python code
samples) for various topics, including data and statistics,
Python3, regular expressions, Keras, TF2, Matplotlib and Seaborn.
Companion files with source code and figures are included. FEATURES
* Covers extensive topics related to natural language processing
and machine learning * Includes separate appendices on data and
statistics, regular expressions, data visualization, Python, Keras,
TF2, and more * Features companion files with source code and color
figures from the book
This book is for developers who are looking for an overview of
basic concepts in Natural Language Processing. It casts a wide net
of techniques to help developers who have a range of technical
backgrounds. Numerous code samples and listings are included to
support myriad topics. The first chapter shows you various details
of managing data that are relevant for NLP. The next pair of
chapters contain NLP concepts, followed by another pair of chapters
with Python code samples to illustrate those NLP concepts. Chapter
6 explores applications, e.g., sentiment analysis, recommender
systems, COVID-19 analysis, spam detection, and a short discussion
regarding chatbots. The final chapter presents the Transformer
architecture, BERT-based models, and the GPT family of models, all
of which were developed during the past three years and considered
SOTA ("state of the art"). The appendices contain introductory
material (including Python code samples) on regular expressions and
probability/statistical concepts. Companion files with source code
and figures are included. FEATURES: Covers extensive topics related
to natural language processing Includes separate appendices on
regular expressions and probability/statistics Features companion
files with source code and figures from the book.
As part of the best-selling Pocket Primer series, this book
provides an overview of the major concepts to program using
TensorFlow 2. The focus of this book is on basic programming
instructions used in machine learning and deep learning. Includes
companion files with source code (in Python) and figures. FEATURES:
Provides an overview of the most important TensorFlow programming
techniques used for machine learning, deep learning, linear and
logical regression, etc. Covers TensorFlow 2 and previous versions
that run with TF2. Includes companion files with source code (in
Python) and figures.
This book is intended primarily for developers who have little or
no experience with Python or Pandas. It contains a fast-paced
introduction to Python and Python-based solutions to various tasks.
Chapter 1 provides a quick tour of basic Python 3, followed by a
chapter that shows how to work with loops and conditional logic in
Python. Chapter 3 discusses data structures in Python, followed by
a chapter that features code samples for tasks with strings and
arrays in Python. Chapter 5 contains concepts in object-oriented
programming, along with code samples that illustrate how they are
implemented in Python. Chapter 6 introduces recursion and some
fundamental topics in combinatorics. Finally, the appendix provides
an introduction to Pandas. Companion files with code and figures
are available for downloading from the publisher. Features:
Provides the reader with basic Python 3 and Pandas programming
concepts. Companion files with code and figures
As part of the best-selling Pocket Primer series, this book is
primarily for data scientists and machine learning engineers who
want to expand their current knowledge of SQL using MySQL as the
primary RDBMS. It includes Python-based code samples to access data
from a MySQL table in a Pandas data frame and Java-based code
samples for accessing data in a MySQL database, along with XML
documents and JSON documents. The book also introduces NoSQL,
presents an overview of MongoDB, and SQLite--an open-source RDBMS
availableon mobile devices. The final chapter of the book covers a
diverse set of miscellaneous topics, such as normalization,
schemas, database optimization, and performance. Numerous code
samples and listings are included to support myriad topics.
Companion files with source code and figures are available from the
publisher. FEATURES: Covers extensive topics related to SQL, using
MySQL as the primary RDBMS Introduces NoSQL, presents an overview
of MongoDB, and SQLite--an open-source RDBMS available on mobile
devices Features companion files with source code and figures from
the book
This book provides readers with enough information for them to
develop more sophisticated Angular applications that incorporate
deep learning. The first three chapters of this book contain a
short tour of basic Angular functionality, such as UI components
and forms in Angular applications. The fourth chapter introduces
you to deep learning, the problems it can solve, and some
challenges for the future. You will also learn about MLPs (Multi
Layer Perceptrons), CNNs (Convolutional Neural Networks), and a
Keras-based code sample of a CNN with the MNIST dataset. The fifth
chapter discusses RNNs (Recurrent Neural Networks), BPTT (Back
Propagation Through Time), as well as LSTMs (Long Short Term
Memory) and AEs (Auto Encoders). The sixth chapter introduces basic
TensorFlow concepts, followedby tensorflowjs (i.e., TensorFlow in
modern browsers), and some examples of Angular applications
combined with deep learning.
As part of the best-selling Pocket Primer Series, this book is
designed to introduce the reader to the basic concepts of data
science using Python 3 and other computer applications. It is
intended to be a fast-paced introduction to some basic features of
data analytics and also covers statistics, data visualization,
linear algebra, and regular expressions. The book includes numerous
code samples using Python, NumPy, R, SQL, NoSQL, and Pandas.
Companion files with source code and color figures are available.
FEATURES: Includes a concise introduction to Python 3 and linear
algebra Provides a thorough introduction to data visualization and
regular expressions Covers NumPy, Pandas, R, and SQL Introduces
probability and statistical concepts Features numerous code samples
throughout Includes companion files with source code and figures
This book is designed to provide the reader with basic Python3
programming concepts related to machine learning. The first four
chapters provide a fast-paced introduction to Python 3, NumPy, and
Pandas. The fifth chapter introduces the fundamental concepts of
machine learning. The sixth chapter is devoted to machine learning
classifiers, such as logistic regression, k-NN, decision trees,
random forests, and SVMs. The final chapter includes material on
NLP and RL. Keras-based code samples are included to supplement the
theoretical discussion. The book also contains separate appendices
for regular expressions, Keras, and TensorFlow 2. Features:
Provides the reader withbasic Python3 programming concepts related
to machine learning Includes separate appendices for regular
expressions, Keras, and TensorFlow 2
As part of the new Pocket Primer series, this book provides an
overview of the major aspects and the source code to use Python 2.
It covers the latest Python developments, built-in functions and
custom classes, data visualization, graphics, databases, and more.
It includes a companion disc with appendices, source code, and
figures. This Pocket Primer is primarily for self-directed learners
who want to learn Python 2 and it serves as a starting point for
deeper exploration of Python programming. Features: Includes a
companion disc with appendices, source code, and figures Contains
material devoted to Raspberry Pi, Roomba, JSON, and Jython Includes
latest Python 2 developments, built-in functions and custom
classes, data visualization, graphics, databases, and more Provides
a solid introduction to Python 2 via complete code samples On the
CD-ROM: Appendices (HTML5 and JavaScript Toolkits, Jython, SPA)
Source code samples All images from the text (including 4-color)
Solutions to Odd-Numbered Exercises
This book begins with an introduction to AI, followed by machine
learning, deep learning, NLP, and reinforcement learning. Readers
will learn about machine learningclassifiers such as logistic
regression, k-NN, decision trees, random forests,and SVMs. Next,
the book covers deep learning architectures such as CNNs,
RNNs,LSTMs, and auto encoders. Keras-based code samples are
included to supplementthe theoretical discussion. In addition, this
book contains appendices forKeras, TensorFlow 2, and Pandas.
As part of the best-selling Pocket Primer series, this book is
designed to introduce the reader to the basic concepts of data
analytics using Python 3. It is intended to be a fast-paced
introduction to some basic features of data analytics and also
covers statistics, data visualization, and data cleaning. The book
includes numerous code samples using NumPy, Pandas, Matplotlib,
Seaborn, and features an appendix on regular expressions. Companion
files with source code and color figures are available. FEATURES:
Includes a concise introduction to Python 3. Provides a thorough
introduction to data and data cleaning. Covers NumPy and Pandas.
Introduces statistical concepts and data visualization
(Matplotlib/Seaborn). Features an appendix on regular expressions.
Includes companion files with source code and figures.
As part of the new Pocket Primer series, this book provides an
overview of the major aspects, the source code, and tutorial videos
to use jQuery. DVD with code, videos, and graphics included.
Features: Integrated coverage of CSS3, jQuery and other important
JS toolkits Covers jQuery Mobile and HTML5 hybrid mobile apps
Covers BackboneJS and Twitter Bootstrap Includes companion DVD with
source code, tutorial videos, and 4-color graphics
As part of the best-selling Pocket Primer series, this book is
designed to prepare programmers for machine learning and deep
learning/TensorFlow topics. It begins with a quick introduction to
Python, followed by chapters that discuss NumPy, Pandas,
Matplotlib, and scikit-learn. The final two chapters contain an
assortment of TensorFlow 1.x code samples, including detailed code
samples for TensorFlow Dataset (which is used heavily in TensorFlow
2 as well). A TensorFlow Dataset refers to the classes in the
tf.data.Dataset namespace that enables programmers to construct a
pipeline of data by means of method chaining so-called lazy
operators, e.g., map(), filter(), batch(), and so forth, based on
data from one or more data sources. Companion files with source
code are available for downloading from the publisher. FEATURES, A
practical introduction to Python, NumPy, Pandas, Matplotlib, and
introductory aspects of TensorFlow 1.x, Contains relevant
NumPy/Pandas code samples that are typical in machine learning
topics, and also useful TensorFlow 1.x code samples for deep
learning/TensorFlow topics, Includes many examples of TensorFlow
Dataset APIs with lazy operators, e.g., map(), filter(), batch(),
take() and also method chaining such operators, Assumes the reader
has very limited experience, Includes companion files with all of
the source code examples (download from the publisher).
As part of the Pocket Primer series, this book provides an overview
of the major aspects and the source code to use D3. This Pocket
Primer is primarily for self-directed learners who want to learn D3
and serves as a starting point for deeper exploration of its
programming. Features: Includes a companion disc with appendices,
source code, and figures Contains material devoted to D3 on mobile
devices, using D3 with Ajax, HTML5 Web Sockets, NodeJS, and covers
D3 application programming interfaces and other toolkits Provides a
solid introduction to D3 via complete code samples eBook Customers:
Companion files are available for downloading with order
number/proof of purchase by writing to the publisher at
[email protected].
As part of the new Pocket Primer series, this book provides an
overview of the major aspects, the source code, and tutorial videos
to use HTML5. DVD with code, videos, and graphics included. eBook
Customers: Companion files are available for downloading with order
number/proof of purchase by writing to the publisher at
[email protected]. Table of Contents: 1. HTML5 Semantic Markup.
2. Introduction to CSS3. 3. SVG Essentials. 4. Introduction to
HTML5 Canvas. 5. Media and Hardware Support HTML5. 6. Storage,
Databases, GeoLocation, and Offline Apps. 7. Browser-Server
Communication. 8. Miscellaneous HTML5 APIs. 9. HTML5 Mobile Apps on
Android and iOS. 10. jQuery Concepts. Appendix A. jQuery Concepts
(Part 2). Appendix B. Introduction to Android.
As part of the best selling Pocket Primer series, this book
provides an overview of the major aspects and the source code to
use the latest versions of Android. It has coverage of the
fundamental aspects of Android that are illustrated via code
samples for versions 4.x through 7.x and features the Google Pixel
phone. This Pocket Primer is primarily for self-directed learners
who want to learn Android programming and it serves as a starting
point for deeper exploration of its numerous applications.
Companion disc (also available for downloading from the publisher)
with source code, images, and appendices. Features: Contains latest
material on Android VR, graphics/animation, apps, and features the
new Google Pixel phone Includes companion files with all of the
source code, appendices, and images from the book Provides coverage
of the fundamental aspects of Android that are illustrated via code
samples for versions 4.x through 7.x On the Companion Files: Source
code samples All images from the text (including 4-color)
Appendices
As part of the best-selling Pocket Primer series, this book is
designed to introduce beginners to TensorFlow 1.x fundamentals for
basic machine learning algorithms in TensorFlow. It is intended to
be a fast-paced introduction to various ""core""features of
TensorFlow, with code samples that cover deep learning and
TensorFlow basics. The material in the chapters illustrates how to
solve a variety of tasks after which you can do further reading to
deepen your knowledge. Companion files with all of the code samples
are available for downloading from the publisher by writing to
[email protected]. Features: Uses Python for code samples
Covers TensorFlow APIs and Datasets Assumes the reader has very
limited experience Companion files with all of the source code
examples (download from the publisher)
Covers the features of HTML5, CSS3 graphics, jQuery, and jQuery
Mobile, and also shows how you can extend the power of CSS3 with
SVG. Designed for readers with some knowledge of
CSS/HTML/JavaScript, but more advanced users will benefit from
numerous graphics techniques that are illustrated in many code
samples. DVD with code and graphics included. You'll see examples
that help you learn to: create mobile Web applications using jQuery
and jQuery Mobile; render HTML5/CSS3/SVG Web pages in Android and
iOS; and create 2D/3D graphics & animation effects with CSS3. A
companion DVD with source code and graphics is included. Features:
Learn how to create Web Pages with jQuery and jQuery Mobile Create
mobile apps in Android and iOS with HTML5/CSS3/SVG Create Web Pages
with jQuery with CSS3 Learn about upcoming CSS3 features such as
CSS3 Shaders and Regions Create 2D/3D graphics and animation
effects with CSS3 Render 2D shapes, charts, and graphs with
gradients in HTML5 Canvas Includes companion DVD with source code
and 4-color graphics
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