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Delve into neural networks, implement deep learning algorithms, and
explore layers of data abstraction with the help of TensorFlow. Key
Features Learn how to implement advanced techniques in deep
learning with Google's brainchild, TensorFlow Explore deep neural
networks and layers of data abstraction with the help of this
comprehensive guide Gain real-world contextualization through some
deep learning problems concerning research and application Book
DescriptionDeep learning is a branch of machine learning algorithms
based on learning multiple levels of abstraction. Neural networks,
which are at the core of deep learning, are being used in
predictive analytics, computer vision, natural language processing,
time series forecasting, and to perform a myriad of other complex
tasks. This book is conceived for developers, data analysts,
machine learning practitioners and deep learning enthusiasts who
want to build powerful, robust, and accurate predictive models with
the power of TensorFlow, combined with other open source Python
libraries. Throughout the book, you'll learn how to develop deep
learning applications for machine learning systems using
Feedforward Neural Networks, Convolutional Neural Networks,
Recurrent Neural Networks, Autoencoders, and Factorization
Machines. Discover how to attain deep learning programming on GPU
in a distributed way. You'll come away with an in-depth knowledge
of machine learning techniques and the skills to apply them to
real-world projects. What you will learn Apply deep machine
intelligence and GPU computing with TensorFlow Access public
datasets and use TensorFlow to load, process, and transform the
data Discover how to use the high-level TensorFlow API to build
more powerful applications Use deep learning for scalable object
detection and mobile computing Train machines quickly to learn from
data by exploring reinforcement learning techniques Explore active
areas of deep learning research and applications Who this book is
forThe book is for people interested in machine learning and
machine intelligence. A rudimentary level of programming in one
language is assumed, as is a basic familiarity with computer
science techniques and technologies, including a basic awareness of
computer hardware and algorithms. Some competence in mathematics is
needed to the level of elementary linear algebra and calculus.
Delve into neural networks, implement deep learning algorithms, and
explore layers of data abstraction with the help of this
comprehensive TensorFlow guide About This Book * Learn how to
implement advanced techniques in deep learning with Google's
brainchild, TensorFlow * Explore deep neural networks and layers of
data abstraction with the help of this comprehensive guide *
Real-world contextualization through some deep learning problems
concerning research and application Who This Book Is For The book
is intended for a general audience of people interested in machine
learning and machine intelligence. A rudimentary level of
programming in one language is assumed, as is a basic familiarity
with computer science techniques and technologies, including a
basic awareness of computer hardware and algorithms. Some
competence in mathematics is needed to the level of elementary
linear algebra and calculus. What You Will Learn * Learn about
machine learning landscapes along with the historical development
and progress of deep learning * Learn about deep machine
intelligence and GPU computing with the latest TensorFlow 1.x *
Access public datasets and utilize them using TensorFlow to load,
process, and transform data * Use TensorFlow on real-world
datasets, including images, text, and more * Learn how to evaluate
the performance of your deep learning models * Using deep learning
for scalable object detection and mobile computing * Train machines
quickly to learn from data by exploring reinforcement learning
techniques * Explore active areas of deep learning research and
applications In Detail Deep learning is the step that comes after
machine learning, and has more advanced implementations. Machine
learning is not just for academics anymore, but is becoming a
mainstream practice through wide adoption, and deep learning has
taken the front seat. As a data scientist, if you want to explore
data abstraction layers, this book will be your guide. This book
shows how this can be exploited in the real world with complex raw
data using TensorFlow 1.x. Throughout the book, you'll learn how to
implement deep learning algorithms for machine learning systems and
integrate them into your product offerings, including search, image
recognition, and language processing. Additionally, you'll learn
how to analyze and improve the performance of deep learning models.
This can be done by comparing algorithms against benchmarks, along
with machine intelligence, to learn from the information and
determine ideal behaviors within a specific context. After
finishing the book, you will be familiar with machine learning
techniques, in particular the use of TensorFlow for deep learning,
and will be ready to apply your knowledge to research or commercial
projects. Style and approach This step-by-step guide will explore
common, and not so common, deep neural networks and show how these
can be exploited in the real world with complex raw data. With the
help of practical examples, you will learn how to implement
different types of neural nets to build smart applications related
to text, speech, and image data processing.
Master efficient parallel programming to build powerful
applications using Python About This Book * Design and implement
efficient parallel software * Master new programming techniques to
address and solve complex programming problems * Explore the world
of parallel programming with this book, which is a go-to resource
for different kinds of parallel computing tasks in Python, using
examples and topics covered in great depth Who This Book Is For
Python Parallel Programming Cookbook is intended for software
developers who are well versed with Python and want to use parallel
programming techniques to write powerful and efficient code. This
book will help you master the basics and the advanced of parallel
computing. What You Will Learn * Synchronize multiple threads and
processes to manage parallel tasks * Implement message passing
communication between processes to build parallel applications *
Program your own GPU cards to address complex problems * Manage
computing entities to execute distributed computational tasks *
Write efficient programs by adopting the event-driven programming
model * Explore the cloud technology with DJango and Google App
Engine * Apply parallel programming techniques that can lead to
performance improvements In Detail This book will teach you
parallel programming techniques using examples in Python and will
help you explore the many ways in which you can write code that
allows more than one process to happen at once. Starting with
introducing you to the world of parallel computing, it moves on to
cover the fundamentals in Python. This is followed by exploring the
thread-based parallelism model using the Python threading module by
synchronizing threads and using locks, mutex, semaphores queues,
GIL, and the thread pool. Next you will be taught about
process-based parallelism where you will synchronize processes
using message passing along with learning about the performance of
MPI Python Modules. You will then go on to learn the asynchronous
parallel programming model using the Python asyncio module along
with handling exceptions. Moving on, you will discover distributed
computing with Python, and learn how to install a broker, use
Celery Python Module, and create a worker. You will understand
anche Pycsp, the Scoop framework, and disk modules in Python.
Further on, you will learnGPU programming withPython using the
PyCUDA module along with evaluating performance limitations. Style
and approach A step-by-step guide to parallel programming using
Python, with recipes accompanied by one or more programming
examples. It is a practically oriented book and has all the
necessary underlying parallel computing concepts.
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