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Machine Learning End To End Guide For Java Developers

Machine Learning  End to End guide for Java developers PDF
Author: Richard M. Reese
Publisher: Packt Publishing Ltd
Release: 2017-10-05
ISBN: 178862940X
Size: 48.73 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 1159
View: 3388

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Machine Learning End To End Guide For Java Developers

by Richard M. Reese, Machine Learning End To End Guide For Java Developers Books available in PDF, EPUB, Mobi Format. Download Machine Learning End To End Guide For Java Developers books, Develop, Implement and Tuneup your Machine Learning applications using the power of Java programming About This Book Detailed coverage on key machine learning topics with an emphasis on both theoretical and practical aspects Address predictive modeling problems using the most popular machine learning Java libraries A comprehensive course covering a wide spectrum of topics such as machine learning and natural language through practical use-cases Who This Book Is For This course is the right resource for anyone with some knowledge of Java programming who wants to get started with Data Science and Machine learning as quickly as possible. If you want to gain meaningful insights from big data and develop intelligent applications using Java, this course is also a must-have. What You Will Learn Understand key data analysis techniques centered around machine learning Implement Java APIs and various techniques such as classification, clustering, anomaly detection, and more Master key Java machine learning libraries, their functionality, and various kinds of problems that can be addressed using each of them Apply machine learning to real-world data for fraud detection, recommendation engines, text classification, and human activity recognition Experiment with semi-supervised learning and stream-based data mining, building high-performing and real-time predictive models Develop intelligent systems centered around various domains such as security, Internet of Things, social networking, and more In Detail Machine Learning is one of the core area of Artificial Intelligence where computers are trained to self-learn, grow, change, and develop on their own without being explicitly programmed. In this course, we cover how Java is employed to build powerful machine learning models to address the problems being faced in the world of Data Science. The course demonstrates complex data extraction and statistical analysis techniques supported by Java, applying various machine learning methods, exploring machine learning sub-domains, and exploring real-world use cases such as recommendation systems, fraud detection, natural language processing, and more, using Java programming. The course begins with an introduction to data science and basic data science tasks such as data collection, data cleaning, data analysis, and data visualization. The next section has a detailed overview of statistical techniques, covering machine learning, neural networks, and deep learning. The next couple of sections cover applying machine learning methods using Java to a variety of chores including classifying, predicting, forecasting, market basket analysis, clustering stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, and deep learning. The last section highlights real-world test cases such as performing activity recognition, developing image recognition, text classification, and anomaly detection. The course includes premium content from three of our most popular books: Java for Data Science Machine Learning in Java Mastering Java Machine Learning On completion of this course, you will understand various machine learning techniques, different machine learning java algorithms you can use to gain data insights, building data models to analyze larger complex data sets, and incubating applications using Java and machine learning algorithms in the field of artificial intelligence. Style and approach This comprehensive course proceeds from being a tutorial to a practical guide, providing an introduction to machine learning and different machine learning techniques, exploring machine learning with Java libraries, and demonstrating real-world machine learning use cases using the Java platform.



Deep Learning For Nlp And Speech Recognition

Deep Learning for NLP and Speech Recognition PDF
Author: Uday Kamath
Publisher: Springer
Release: 2019-06-10
ISBN: 3030145964
Size: 14.69 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 621
View: 6560

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Deep Learning For Nlp And Speech Recognition

by Uday Kamath, Deep Learning For Nlp And Speech Recognition Books available in PDF, EPUB, Mobi Format. Download Deep Learning For Nlp And Speech Recognition books, This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.



Apache Spark 2 X For Java Developers

Apache Spark 2 x for Java Developers PDF
Author: Sourav Gulati
Publisher: Packt Publishing Ltd
Release: 2017-07-26
ISBN: 178712942X
Size: 19.14 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 350
View: 3567

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Apache Spark 2 X For Java Developers

by Sourav Gulati, Apache Spark 2 X For Java Developers Books available in PDF, EPUB, Mobi Format. Download Apache Spark 2 X For Java Developers books, Unleash the data processing and analytics capability of Apache Spark with the language of choice: Java About This Book Perform big data processing with Spark—without having to learn Scala! Use the Spark Java API to implement efficient enterprise-grade applications for data processing and analytics Go beyond mainstream data processing by adding querying capability, Machine Learning, and graph processing using Spark Who This Book Is For If you are a Java developer interested in learning to use the popular Apache Spark framework, this book is the resource you need to get started. Apache Spark developers who are looking to build enterprise-grade applications in Java will also find this book very useful. What You Will Learn Process data using different file formats such as XML, JSON, CSV, and plain and delimited text, using the Spark core Library. Perform analytics on data from various data sources such as Kafka, and Flume using Spark Streaming Library Learn SQL schema creation and the analysis of structured data using various SQL functions including Windowing functions in the Spark SQL Library Explore Spark Mlib APIs while implementing Machine Learning techniques to solve real-world problems Get to know Spark GraphX so you understand various graph-based analytics that can be performed with Spark In Detail Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing. While Spark is built on Scala, the Spark Java API exposes all the Spark features available in the Scala version for Java developers. This book will show you how you can implement various functionalities of the Apache Spark framework in Java, without stepping out of your comfort zone. The book starts with an introduction to the Apache Spark 2.x ecosystem, followed by explaining how to install and configure Spark, and refreshes the Java concepts that will be useful to you when consuming Apache Spark's APIs. You will explore RDD and its associated common Action and Transformation Java APIs, set up a production-like clustered environment, and work with Spark SQL. Moving on, you will perform near-real-time processing with Spark streaming, Machine Learning analytics with Spark MLlib, and graph processing with GraphX, all using various Java packages. By the end of the book, you will have a solid foundation in implementing components in the Spark framework in Java to build fast, real-time applications. Style and approach This practical guide teaches readers the fundamentals of the Apache Spark framework and how to implement components using the Java language. It is a unique blend of theory and practical examples, and is written in a way that will gradually build your knowledge of Apache Spark.



Big Data Analytics With Java

Big Data Analytics with Java PDF
Author: Rajat Mehta
Publisher: Packt Publishing Ltd
Release: 2017-07-31
ISBN: 1787282198
Size: 71.33 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 418
View: 6768

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Big Data Analytics With Java

by Rajat Mehta, Big Data Analytics With Java Books available in PDF, EPUB, Mobi Format. Download Big Data Analytics With Java books, Learn the basics of analytics on big data using Java, machine learning and other big data tools About This Book Acquire real-world set of tools for building enterprise level data science applications Surpasses the barrier of other languages in data science and learn create useful object-oriented codes Extensive use of Java compliant big data tools like apache spark, Hadoop, etc. Who This Book Is For This book is for Java developers who are looking to perform data analysis in production environment. Those who wish to implement data analysis in their Big data applications will find this book helpful. What You Will Learn Start from simple analytic tasks on big data Get into more complex tasks with predictive analytics on big data using machine learning Learn real time analytic tasks Understand the concepts with examples and case studies Prepare and refine data for analysis Create charts in order to understand the data See various real-world datasets In Detail This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naive Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world. Style and approach The approach of book is to deliver practical learning modules in manageable content. Each chapter is a self-contained unit of a concept in big data analytics. Book will step by step builds the competency in the area of big data analytics. Examples using real world case studies to give ideas of real applications and how to use the techniques mentioned. The examples and case studies will be shown using both theory and code.



The Definitive Guide To Modern Java Clients With Javafx

The Definitive Guide to Modern Java Clients with JavaFX PDF
Author: Stephen Chin
Publisher: Apress
Release: 2019-11-12
ISBN: 1484249267
Size: 19.22 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 621
View: 3212

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The Definitive Guide To Modern Java Clients With Javafx

by Stephen Chin, The Definitive Guide To Modern Java Clients With Javafx Books available in PDF, EPUB, Mobi Format. Download The Definitive Guide To Modern Java Clients With Javafx books, Build enhanced visual experiences and design and deploy modern, easy-to-maintain, client applications across a variety of platforms. This book will show you how these applications can take advantage of the latest user interface components, 3D technology, and cloud services to create immersive visualizations and allow high-value data manipulation. The Definitive Guide to Modern Java Clients with JavaFX is a professional reference for building Java applications for desktop, mobile, and embedded in the Cloud age. It offers end-to-end coverage of the latest features in JavaFX and Java 13. After reading this book, you will be equipped to upgrade legacy client applications, develop cross-platform applications in Java, and build enhanced desktop and mobile native clients. What You Will Learn Create modern client applications in Java using the latest JavaFX and Java 13 Build enterprise clients that will enable integration with existing cloud services Use advanced visualization and 3D features Deploy on desktop, mobile, and embedded devices Who This Book Is For Professional Java developers who are interested in learning the latest client Java development techniques to fill out their skillset.



Java Deep Learning Cookbook

Java Deep Learning Cookbook PDF
Author: Rahul Raj
Publisher: Packt Publishing Ltd
Release: 2019-11-08
ISBN: 1788999479
Size: 61.27 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages : 304
View: 3387

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Java Deep Learning Cookbook

by Rahul Raj, Java Deep Learning Cookbook Books available in PDF, EPUB, Mobi Format. Download Java Deep Learning Cookbook books, Use Java and Deeplearning4j to build robust, scalable, and highly accurate AI models from scratch Key Features Install and configure Deeplearning4j to implement deep learning models from scratch Explore recipes for developing, training, and fine-tuning your neural network models in Java Model neural networks using datasets containing images, text, and time-series data Book Description Java is one of the most widely used programming languages in the world. With this book, you will see how to perform deep learning using Deeplearning4j (DL4J) – the most popular Java library for training neural networks efficiently. This book starts by showing you how to install and configure Java and DL4J on your system. You will then gain insights into deep learning basics and use your knowledge to create a deep neural network for binary classification from scratch. As you progress, you will discover how to build a convolutional neural network (CNN) in DL4J, and understand how to construct numeric vectors from text. This deep learning book will also guide you through performing anomaly detection on unsupervised data and help you set up neural networks in distributed systems effectively. In addition to this, you will learn how to import models from Keras and change the configuration in a pre-trained DL4J model. Finally, you will explore benchmarking in DL4J and optimize neural networks for optimal results. By the end of this book, you will have a clear understanding of how you can use DL4J to build robust deep learning applications in Java. What you will learn Perform data normalization and wrangling using DL4J Build deep neural networks using DL4J Implement CNNs to solve image classification problems Train autoencoders to solve anomaly detection problems using DL4J Perform benchmarking and optimization to improve your model's performance Implement reinforcement learning for real-world use cases using RL4J Leverage the capabilities of DL4J in distributed systems Who this book is for If you are a data scientist, machine learning developer, or a deep learning enthusiast who wants to implement deep learning models in Java, this book is for you. Basic understanding of Java programming as well as some experience with machine learning and neural networks is required to get the most out of this book.



Tensorflow Deep Learning Projects

TensorFlow Deep Learning Projects PDF
Author: Alexey Grigorev
Publisher: Packt Publishing Ltd
Release: 2018-03-28
ISBN: 1788398386
Size: 19.76 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 320
View: 552

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Tensorflow Deep Learning Projects

by Alexey Grigorev, Tensorflow Deep Learning Projects Books available in PDF, EPUB, Mobi Format. Download Tensorflow Deep Learning Projects books, 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 Description TensorFlow 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 for This 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.



Oracle Jdeveloper 10g For Forms Pl Sql Developers A Guide To Web Development With Oracle Adf

Oracle JDeveloper 10g for Forms   PL SQL Developers  A Guide to Web Development with Oracle ADF PDF
Author: Peter Koletzke
Publisher: McGraw Hill Professional
Release: 2006-09-28
ISBN: 0071710477
Size: 78.46 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 562
View: 4565

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Oracle Jdeveloper 10g For Forms Pl Sql Developers A Guide To Web Development With Oracle Adf

by Peter Koletzke, Oracle Jdeveloper 10g For Forms Pl Sql Developers A Guide To Web Development With Oracle Adf Books available in PDF, EPUB, Mobi Format. Download Oracle Jdeveloper 10g For Forms Pl Sql Developers A Guide To Web Development With Oracle Adf books, The most efficient way to learn J2EE programming techniques Two Oracle experts demonstrate techniques for working within J2EE and JDeveloper for the thousands of developers currently using Oracle Forms and the PL/SQL language. Oracle is shifting their focus towards Java technologies, so you will need to know how to use the Java-based J2EE and JDeveloper. The book includes explanations of the Application Development Framework (ADF). Throughout, high-level and low-level Forms concepts are related to Java concepts so that you can become comfortable with the new terminology. Covers the new components required when developing and deploying a J2EE application Special mentions in the text describe how JDeveloper techniques translate to PL/SQL or Oracle Forms Covers JDeveloper 10.1.3 and ADF Faces All code and examples will be available online



Deep Learning With Microsoft Cognitive Toolkit Quick Start Guide

Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide PDF
Author: Willem Meints
Publisher: Packt Publishing Ltd
Release: 2019-03-28
ISBN: 1789803195
Size: 10.53 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 208
View: 5065

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Deep Learning With Microsoft Cognitive Toolkit Quick Start Guide

by Willem Meints, Deep Learning With Microsoft Cognitive Toolkit Quick Start Guide Books available in PDF, EPUB, Mobi Format. Download Deep Learning With Microsoft Cognitive Toolkit Quick Start Guide books, Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering how you can use deep learning models in your software applications with Microsoft Cognitive Toolkit Key Features Understand the fundamentals of Microsoft Cognitive Toolkit and set up the development environment Train different types of neural networks using Cognitive Toolkit and deploy it to production Evaluate the performance of your models and improve your deep learning skills Book Description Cognitive Toolkit is a very popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks, such as convolutional and recurrent neural networks. This book will help you understand the basics of deep learning. You will learn how to use Microsoft Cognitive Toolkit to build deep learning models and discover what makes this framework unique so that you know when to use it. This book will be a quick, no-nonsense introduction to the library and will teach you how to train different types of neural networks, such as convolutional neural networks, recurrent neural networks, autoencoders, and more, using Cognitive Toolkit. Then we will look at two scenarios in which deep learning can be used to enhance human capabilities. The book will also demonstrate how to evaluate your models' performance to ensure it trains and runs smoothly and gives you the most accurate results. Finally, you will get a short overview of how Cognitive Toolkit fits in to a DevOps environment What you will learn Set up your deep learning environment for the Cognitive Toolkit on Windows and Linux Pre-process and feed your data into neural networks Use neural networks to make effcient predictions and recommendations Train and deploy effcient neural networks such as CNN and RNN Detect problems in your neural network using TensorBoard Integrate Cognitive Toolkit with Azure ML Services for effective deep learning Who this book is for Data Scientists, Machine learning developers, AI developers who wish to train and deploy effective deep learning models using Microsoft CNTK will find this book to be useful. Readers need to have experience in Python or similar object-oriented language like C# or Java.



Deep Learning Practical Neural Networks With Java

Deep Learning  Practical Neural Networks with Java PDF
Author: Yusuke Sugomori
Publisher: Packt Publishing Ltd
Release: 2017-06-08
ISBN: 1788471717
Size: 71.57 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 744
View: 5572

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Deep Learning Practical Neural Networks With Java

by Yusuke Sugomori, Deep Learning Practical Neural Networks With Java Books available in PDF, EPUB, Mobi Format. Download Deep Learning Practical Neural Networks With Java books, 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: Java Deep Learning Essentials Machine Learning in Java 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