java-deep-learning-essentials

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Java Deep Learning Essentials

Author : Yusuke Sugomori
ISBN : 9781785283147
Genre : Computers
File Size : 25. 82 MB
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Dive into the future of data science and learn how to build the sophisticated algorithms that are fundamental to deep learning and AI with Java About This Book Go beyond the theory and put Deep Learning into practice with Java Find out how to build a range of Deep Learning algorithms using a range of leading frameworks including DL4J, Theano and Caffe Whether you're a data scientist or Java developer, dive in and find out how to tackle Deep Learning Who This Book Is For This book is intended for data scientists and Java developers who want to dive into the exciting world of deep learning. It would also be good for machine learning users who intend to leverage deep learning in their projects, working within a big data environment. What You Will Learn Get a practical deep dive into machine learning and deep learning algorithms Implement machine learning algorithms related to deep learning Explore neural networks using some of the most popular Deep Learning frameworks Dive into Deep Belief Nets and Stacked Denoising Autoencoders algorithms Discover more deep learning algorithms with Dropout and Convolutional Neural Networks Gain an insight into the deep learning library DL4J and its practical uses Get to know device strategies to use deep learning algorithms and libraries in the real world Explore deep learning further with Theano and Caffe In Detail AI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Deep Learning algorithms are being used across a broad range of industries – as the fundamental driver of AI, being able to tackle Deep Learning is going to a vital and valuable skill not only within the tech world but also for the wider global economy that depends upon knowledge and insight for growth and success. It's something that's moving beyond the realm of data science – if you're a Java developer, this book gives you a great opportunity to expand your skillset. Starting with an introduction to basic machine learning algorithms, to give you a solid foundation, Deep Learning with Java takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. Once you've got to grips with the fundamental mathematical principles, you'll start exploring neural networks and identify how to tackle challenges in large networks using advanced algorithms. You will learn how to use the DL4J library and apply Deep Learning to a range of real-world use cases. Featuring further guidance and insights to help you solve challenging problems in image processing, speech recognition, language modeling, this book will make you rethink what you can do with Java, showing you how to use it for truly cutting-edge predictive insights. As a bonus, you'll also be able to get to grips with Theano and Caffe, two of the most important tools in Deep Learning today. By the end of the book, you'll be ready to tackle Deep Learning with Java. Wherever you've come from – whether you're a data scientist or Java developer – you will become a part of the Deep Learning revolution! Style and approach This is a step-by-step, practical tutorial that discusses key concepts. This book offers a hands-on approach to key algorithms to help you develop a greater understanding of deep learning. It is packed with implementations from scratch, with detailed explanation that make the concepts easy to understand and follow.

R Deep Learning Essentials

Author : Dr. Joshua F. Wiley
ISBN : 9781785284717
Genre : Computers
File Size : 69. 71 MB
Format : PDF
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Build automatic classification and prediction models using unsupervised learning About This Book Harness the ability to build algorithms for unsupervised data using deep learning concepts with R Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models Build models relating to neural networks, prediction and deep prediction Who This Book Is For This book caters to aspiring data scientists who are well versed with machine learning concepts with R and are looking to explore the deep learning paradigm using the packages available in R. You should have a fundamental understanding of the R language and be comfortable with statistical algorithms and machine learning techniques, but you do not need to be well versed with deep learning concepts. What You Will Learn Set up the R package H2O to train deep learning models Understand the core concepts behind deep learning models Use Autoencoders to identify anomalous data or outliers Predict or classify data automatically using deep neural networks Build generalizable models using regularization to avoid overfitting the training data In Detail Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures. With the superb memory management and the full integration with multi-node big data platforms, the H2O engine has become more and more popular among data scientists in the field of deep learning. This book will introduce you to the deep learning package H2O with R and help you understand the concepts of deep learning. We will start by setting up important deep learning packages available in R and then move towards building models related to neural networks, prediction, and deep prediction, all of this with the help of real-life examples. After installing the H2O package, you will learn about prediction algorithms. Moving ahead, concepts such as overfitting data, anomalous data, and deep prediction models are explained. Finally, the book will cover concepts relating to tuning and optimizing models. Style and approach This book takes a practical approach to showing you the concepts of deep learning with the R programming language. We will start with setting up important deep learning packages available in R and then move towards building models related to neural network, prediction, and deep prediction - and all of this with the help of real-life examples.

Deep Learning Practical Neural Networks With Java

Author : Yusuke Sugomori
ISBN : 9781788471718
Genre : Computers
File Size : 20. 62 MB
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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

R Deep Learning Essentials

Author : Mark Hodnett
ISBN : 9781788997805
Genre : Computers
File Size : 86. 21 MB
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Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for computer vision and text Apply deep learning techniques in cloud for large-scale processing Build, train, and optimize neural network models on a range of datasets Book Description Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics. By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects. What you will learn Build shallow neural network prediction models Prevent models from overfitting the data to improve generalizability Explore techniques for finding the best hyperparameters for deep learning models Create NLP models using Keras and TensorFlow in R Use deep learning for computer vision tasks Implement deep learning tasks, such as NLP, recommendation systems, and autoencoders Who this book is for This second edition of R Deep Learning Essentials is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. Fundamental understanding of the R language is necessary to get the most out of this book.

Intelligent Information And Database Systems

Author : Ngoc Thanh Nguyen
ISBN : 9783319544304
Genre : Computers
File Size : 34. 1 MB
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The two-volume set LNAI 10191 and 10192 constitutes the refereed proceedings of the 9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017, held in Kanazawa, Japan, in April 2017. The total of 152 full papers accepted for publication in these proceedings was carefully reviewed and selected from 420 submissions. They were organized in topical sections named: Knowledge Engineering and Semantic Web; Social Networks and Recommender Systems; Text Processing and Information Retrieval; Intelligent Database Systems; Intelligent Information Systems; Decision Support and Control Systems; Machine Learning and Data Mining; Computer Vision Techniques; Advanced Data Mining Techniques and Applications; Intelligent and Context Systems; Multiple Model Approach to Machine Learning; Applications of Data Science; Artificial Intelligence Applications for E-services; Automated Reasoning and Proving Techniques with Applications in Intelligent Systems; Collective Intelligence for Service Innovation, Technology Opportunity, E-Learning and Fuzzy Intelligent Systems; Intelligent Computer Vision Systems and Applications; Intelligent Data Analysis, Applications and Technologies for Internet of Things; Intelligent Algorithms and Brain Functions; Intelligent Systems and Algorithms in Information Sciences; IT in Biomedicine; Intelligent Technologies in the Smart Cities in the 21st Century; Analysis of Image, Video and Motion Data in Life Sciences; Modern Applications of Machine Learning for Actionable Knowledge Extraction; Mathematics of Decision Sciences and Information Science; Scalable Data Analysis in Bioinformatics and Biomedical Informatics; and Technological Perspective of Agile Transformation in IT organizations.

Clojure Web Development Essentials

Author : Ryan Baldwin
ISBN : 9781784394875
Genre : Computers
File Size : 35. 32 MB
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This book is for anyone who's worked with Clojure and wants to use it to start developing applications for the Web. Experience or familiarity with basic Clojure syntax is a must, and exposure to Leiningen (or other similar build tools such as Maven) would be helpful.

Computing Concepts With Java Essentials

Author : Cay S. Horstmann
ISBN : UOM:49015002684273
Genre : Computers
File Size : 74. 93 MB
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This unique book teaches you the fundamental concepts of good computer programming while introducing you to one of the most powerful languages in use today—Java! It gives you an ideal balance between programming concepts and the details of Java. Rather than exhaustively cover the entire language, the author focuses on a subset of Java—a lean and practical core that is manageable, yet detailed enough to create powerful Java applets. And as you master the basics of Java, you'll be developing solid programming skills that will increase your effectiveness no matter which language you work with! Includes a wealth of practical advice, tips, and reference material.

Machine Learning Bookcamp

Author : Alexey Grigorev
ISBN : 1617296813
Genre :
File Size : 27. 54 MB
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In Machine Learning Bookcamp you’ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. The only way to learn is to practice! In Machine Learning Bookcamp, you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. In Machine Learning Bookcamp you’ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you’ve learned in previous chapters. By the end of the bookcamp, you’ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Tensorflow Deep Learning Projects

Author : Alexey Grigorev
ISBN : 9781788398381
Genre : Computers
File Size : 52. 25 MB
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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 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.

Large Scale Machine Learning With Python

Author : Bastiaan Sjardin
ISBN : 9781785888021
Genre : Computers
File Size : 75. 37 MB
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Learn to build powerful machine learning models quickly and deploy large-scale predictive applications About This Book Design, engineer and deploy scalable machine learning solutions with the power of Python Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful. What You Will Learn Apply the most scalable machine learning algorithms Work with modern state-of-the-art large-scale machine learning techniques Increase predictive accuracy with deep learning and scalable data-handling techniques Improve your work by combining the MapReduce framework with Spark Build powerful ensembles at scale Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine In Detail Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. Style and Approach This efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly. Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production. This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.

Learning Java By Building Android Games

Author : John Horton
ISBN : 9781784393847
Genre : Computers
File Size : 81. 35 MB
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If you are completely new to either Java, Android, or game programming and are aiming to publish Android games, then this book is for you. This book also acts as a refresher for those who already have experience in Java on another platforms or other object-oriented languages.

Spring Batch Essentials

Author : P. Raja Malleswara Rao
ISBN : 9781783553389
Genre : Computers
File Size : 27. 33 MB
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If you are a Java developer with basic knowledge of Spring and some experience in the development of enterprise applications, and want to learn about batch application development in detail, then this book is ideal for you. This book will be perfect as your next step towards building simple yet powerful batch applications on a Java-based platform.

Book Review Index

Author : Gale Group
ISBN : 0787665797
Genre : Literary Criticism
File Size : 42. 82 MB
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'Book Review Index' provides quick access to reviews of books, periodicals, books on tape and electronic media representing a wide range of popular, academic and professional interests. More than 600 publications are indexed, including journals and national general interest publications and newspapers. 'Book Review Index' is available in a three-issue subscription covering the current year or as an annual cumulation covering the past year.

Essentials Of World Regional Geography

Author : Christopher L. Salter
ISBN : 0030336333
Genre : Science
File Size : 45. 11 MB
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Comprehensive and current, the atlas can be packaged with the text.

Scala Programming Projects

Author : Mikael Valot
ISBN : 9781788395342
Genre : Computers
File Size : 70. 67 MB
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Discover unique features and powerful capabilities of Scala Programming as you build projects in a wide range of domains Key Features Develop a range of Scala projects from web applications to big data analysis Leverage full power of modern web programming using Play Framework Build real-time data pipelines in Scala with a Bitcoin transaction analysis app Book Description Scala is a type-safe JVM language that incorporates object-oriented and functional programming (OOP and FP) aspects. This book gets you started with essentials of software development by guiding you through various aspects of Scala programming, helping you bridge the gap between learning and implementing. You will learn about the unique features of Scala through diverse applications and experience simple yet powerful approaches for software development. Scala Programming Projects will help you build a number of applications, beginning with simple projects, such as a financial independence calculator, and advancing to other projects, such as a shopping application and a Bitcoin transaction analyzer. You will be able to use various Scala features, such as its OOP and FP capabilities, and learn how to write concise, reactive, and concurrent applications in a type-safe manner. You will also learn how to use top-notch libraries such as Akka and Play and integrate Scala apps with Kafka, Spark, and Zeppelin, along with deploying applications on a cloud platform. By the end of the book, you will not only know the ins and outs of Scala, but you will also be able to apply it to solve a variety of real-world problems What you will learn Build, test, and package code using Scala Build Tool Decompose code into functions, classes, and packages for maintainability Implement the functional programming capabilities of Scala Develop a simple CRUD REST API using the Play framework Access a relational database using Slick Develop a dynamic web UI using Scala.js Source streaming data using Spark Streaming and write a Kafka producer Use Spark and Zeppelin to analyze data Who this book is for If you are an amateur programmer who wishes to learn how to use Scala, this book is for you. Knowledge of Java will be beneficial, but not necessary, to understand the concepts covered in this book.

Oracle8i

Author : Kevin Loney
ISBN : 0072123648
Genre : Computers
File Size : 36. 22 MB
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Announcing the 10th Anniversary Edition of the best-selling Oracle book of all time--Oracle8i: The Complete Reference! Written by Oracle experts and complete with a fully searchable book and reusable code on CD-ROM, this is the most definitive resource available on Oracle8i. It's a must-have for every Oracle professional.

React Js Essentials

Author : Artemij Fedosejev
ISBN : 9781782174622
Genre : Computers
File Size : 47. 79 MB
Format : PDF, ePub
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A fast-paced guide to designing and building scalable and maintainable web apps with React.js About This Book Build maintainable and performant user interfaces for your web applications using React.js Create reusable React.js components to save time and effort in maintaining your user interfaces Learn how to build a ready-to-deploy React.js web application, following our step-by-step tutorial Who This Book Is For If you're a front-end developer with knowledge of jQuery and its libraries, along with frameworks, such as Angular.JS and Backbone.JS, or native JavaScript development, and you wish to use the fastest web user interface library there is, then this book is ideal for you. What You Will Learn Install powerful React.js tools to make development much more efficient Create React elements with properties and children Get started with stateless and stateful React components Use JSX to speed up your React.js development process Add reactivity to your React components with lifecycle methods Integrate your React components with other JavaScript libraries Utilize the Flux application architecture with your React components Test your React components with Jest test framework In Detail Building web applications with maintainable and performant user interfaces is a challenge that many have faced for more than a decade, but no one has risen to this challenge quite like React.js. Today React.js is used by Facebook, Instagram, Khan Academy, and Imperial College London, to name a few. Many new users recognize the benefits of React.js and adopt it in their own projects, forming a fast-growing community. The speed at which React.js has evolved promises a bright future for those who invest in learning it today. React.js Essentials will take you on a fast-paced journey through building your own maintainable React.js application. Begin by exploring how you can create single and multiple user interface elements. Create stateless and stateful components and make them reactive, learn to interact between your components and lifecycle methods and gauge how to effectively integrate your user interface components with other JavaScript libraries. Delve deep into the core elements of the Flux architecture and learn how to manage your application using stores. Finish by going that extra mile with the Jest test framework, running multiple tests on your application and find solutions to scale it further without complexity. Style and approach The book adopts a step-by-step, hands-on approach with ample codes to ensure you learn React.js at a fast pace.

American Book Publishing Record

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ISBN : STANFORD:36105117254917
Genre : Books
File Size : 24. 42 MB
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The New York Times Book Review

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ISBN : UOM:39015079608280
Genre : American literature
File Size : 28. 10 MB
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Books In Print 2004 2005

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ISBN : 0835246493
Genre : Literature
File Size : 40. 69 MB
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