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Leverage the power of Tensorflow to design deep learning systems
for a variety of real-world scenarios Key Features Build efficient
deep learning pipelines using the popular Tensorflow framework
Train neural networks such as ConvNets, generative models, and
LSTMs Includes projects related to Computer Vision, stock
prediction, chatbots and more Book DescriptionTensorFlow is one of
the most popular frameworks used for machine learning and, more
recently, deep learning. It provides a fast and efficient framework
for training different kinds of deep learning models, with very
high accuracy. This book is your guide to master deep learning with
TensorFlow with the help of 10 real-world projects. TensorFlow Deep
Learning Projects starts with setting up the right TensorFlow
environment for deep learning. Learn to train different types of
deep learning models using TensorFlow, including Convolutional
Neural Networks, Recurrent Neural Networks, LSTMs, and Generative
Adversarial Networks. While doing so, you will build end-to-end
deep learning solutions to tackle different real-world problems in
image processing, recommendation systems, stock prediction, and
building chatbots, to name a few. You will also develop systems
that perform machine translation, and use reinforcement learning
techniques to play games. By the end of this book, you will have
mastered all the concepts of deep learning and their implementation
with TensorFlow, and will be able to build and train your own deep
learning models with TensorFlow confidently. What you will learn
Set up the TensorFlow environment for deep learning Construct your
own ConvNets for effective image processing Use LSTMs for image
caption generation Forecast stock prediction accurately with an
LSTM architecture Learn what semantic matching is by detecting
duplicate Quora questions Set up an AWS instance with TensorFlow to
train GANs Train and set up a chatbot to understand and interpret
human input Build an AI capable of playing a video game by itself
-and win it! Who this book is forThis book is for data scientists,
machine learning developers as well as deep learning practitioners,
who want to build interesting deep learning projects that leverage
the power of Tensorflow. Some understanding of machine learning and
deep learning, and familiarity with the TensorFlow framework is all
you need to get started with this book.
Data collection, processing, analysis, and more About This Book *
Your entry ticket to the world of data science with the stability
and power of Java * Explore, analyse, and visualize your data
effectively using easy-to-follow examples * A highly practical
course covering a broad set of topics - from the basics of Machine
Learning to Deep Learning and Big Data frameworks. Who This Book Is
For This course is meant for Java developers who are comfortable
developing applications in Java, and now want to enter the world of
data science or wish to build intelligent applications. Aspiring
data scientists with some understanding of the Java programming
language will also find this book to be very helpful. If you are
willing to build efficient data science applications and bring them
in the enterprise environment without changing your existing Java
stack, this book is for you! What You Will Learn * Understand the
key concepts of data science * Explore the data science ecosystem
available in Java * Work with the Java APIs and techniques used to
perform efficient data analysis * Find out how to approach
different machine learning problems with Java * Process
unstructured information such as natural language text or images,
and create your own search * Learn how to build deep neural
networks with DeepLearning4j * Build data science applications that
scale and process large amounts of data * Deploy data science
models to production and evaluate their performance In Detail Data
science is concerned with extracting knowledge and insights from a
wide variety of data sources to analyse patterns or predict future
behaviour. It draws from a wide array of disciplines including
statistics, computer science, mathematics, machine learning, and
data mining. In this course, we cover the basic as well as advanced
data science concepts and how they are implemented using the
popular Java tools and libraries.The course starts with an
introduction of data science, followed by the basic data science
tasks of data collection, data cleaning, data analysis, and data
visualization. This is followed by a discussion of statistical
techniques and more advanced topics including machine learning,
neural networks, and deep learning. You will examine the major
categories of data analysis including text, visual, and audio data,
followed by a discussion of resources that support parallel
implementation. Throughout this course, the chapters will
illustrate a challenging data science problem, and then go on to
present a comprehensive, Java-based solution to tackle that
problem. You will cover a wide range of topics - from
classification and regression, to dimensionality reduction and
clustering, deep learning and working with Big Data. Finally, you
will see the different ways to deploy the model and evaluate it in
production settings. By the end of this course, you will be up and
running with various facets of data science using Java, in no time
at all. This course contains premium content from two of our
recently published popular titles: * Java for Data Science *
Mastering Java for Data Science Style and approach This course
follows a tutorial approach, providing examples of each of the
concepts covered. With a step-by-step instructional style, this
book covers various facets of data science and will get you up and
running quickly.
Use Java to create a diverse range of Data Science applications and
bring Data Science into production About This Book * An overview of
modern Data Science and Machine Learning libraries available in
Java * Coverage of a broad set of topics, going from the basics of
Machine Learning to Deep Learning and Big Data frameworks. *
Easy-to-follow illustrations and the running example of building a
search engine. Who This Book Is For This book is intended for
software engineers who are comfortable with developing Java
applications and are familiar with the basic concepts of data
science. Additionally, it will also be useful for data scientists
who do not yet know Java but want or need to learn it. If you are
willing to build efficient data science applications and bring them
in the enterprise environment without changing the existing stack,
this book is for you! What You Will Learn * Get a solid
understanding of the data processing toolbox available in Java *
Explore the data science ecosystem available in Java * Find out how
to approach different machine learning problems with Java * Process
unstructured information such as natural language text or images *
Create your own search engine * Get state-of-the-art performance
with XGBoost * Learn how to build deep neural networks with
DeepLearning4j * Build applications that scale and process large
amounts of data * Deploy data science models to production and
evaluate their performance In Detail Java is the most popular
programming language, according to the TIOBE index, and it is a
typical choice for running production systems in many companies,
both in the startup world and among large enterprises. Not
surprisingly, it is also a common choice for creating data science
applications: it is fast and has a great set of data processing
tools, both built-in and external. What is more, choosing Java for
data science allows you to easily integrate solutions with existing
software, and bring data science into production with less effort.
This book will teach you how to create data science applications
with Java. First, we will revise the most important things when
starting a data science application, and then brush up the basics
of Java and machine learning before diving into more advanced
topics. We start by going over the existing libraries for data
processing and libraries with machine learning algorithms. After
that, we cover topics such as classification and regression,
dimensionality reduction and clustering, information retrieval and
natural language processing, and deep learning and big data.
Finally, we finish the book by talking about the ways to deploy the
model and evaluate it in production settings. Style and approach
This is a practical guide where all the important concepts such as
classification, regression, and dimensionality reduction are
explained with the help of examples.
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