Build and run intelligent applications by leveraging key Java
machine learning libraries About This Book * Develop a sound
strategy to solve predictive modelling problems using the most
popular machine learning Java libraries. * Explore a broad variety
of data processing, machine learning, and natural language
processing through diagrams, source code, and real-world
applications * This step-by-step guide will help you solve
real-world problems and links neural network theory to their
application Who This Book Is For This course is intended for data
scientists and Java developers who want to dive into the exciting
world of deep learning. It will get you up and running quickly and
provide you with the skills you need to successfully create,
customize, and deploy machine learning applications in real life.
What You Will Learn * Get a practical deep dive into machine
learning and deep learning algorithms * Explore neural networks
using some of the most popular Deep Learning frameworks * Dive into
Deep Belief Nets and Stacked Denoising Autoencoders algorithms *
Apply machine learning to fraud, anomaly, and outlier detection *
Experiment with deep learning concepts, algorithms, and the toolbox
for deep learning * Select and split data sets into training, test,
and validation, and explore validation strategies * Apply the code
generated in practical examples, including weather forecasting and
pattern recognition In Detail Machine learning applications are
everywhere, from self-driving cars, spam detection, document
search, and trading strategies, to speech recognitionStarting with
an introduction to basic machine learning algorithms, this course
takes you further into this vital world of stunning predictive
insights and remarkable machine intelligence. This course helps you
solve challenging problems in image processing, speech recognition,
language modeling. You will discover how to detect anomalies and
fraud, and ways to perform activity recognition, image recognition,
and text. You will also work with examples such as weather
forecasting, disease diagnosis, customer profiling, generalization,
extreme machine learning and more. By the end of this course, you
will have all the knowledge you need to perform deep learning on
your system with varying complexity levels, to apply them to your
daily work. The course provides you with highly practical content
explaining deep learning with Java, from the following Packt books:
1. Java Deep Learning Essentials 2. Machine Learning in Java 3.
Neural Network Programming with Java, Second Edition Style and
approach This course aims to create a smooth learning path that
will teach you how to effectively use deep learning with Java with
other de facto components to get the most out of it. Through this
comprehensive course, you'll learn the basics of predictive
modelling and progress to solve real-world problems and links
neural network theory to their application
General
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