Publisher: 24

Java. Ćwiczenia zaawansowane. Wydanie II

Marcin Lis

Mistrzostwo w Javie w zasięgu Twoich rąk! Na czym polegają programowanie współbieżne i obsługa pracy wątków? Jak zapewnić właściwą komunikację sieciową z użyciem protokołu wymiany danych? Co zrobić, by współpraca z relacyjnymi bazami danych czy obsługa zapytań SQL przebiegały idealnie? Rzadko zdarza się, by jeden język programowania był w stanie zaspokoić niemal wszystkie potrzeby obsługi bardzo różnych urządzeń i systemów operacyjnych bez konieczności żmudnego dostosowywania. Java znakomicie spełnia te wymagania i właśnie dlatego każdy programista - nawet taki, który używa w swojej pracy innych języków programowania - powinien poznać ją bardzo dokładnie. A najłatwiejszym i najbardziej efektywnym sposobem poszerzenia wiedzy w dziedzinie programowania jest przećwiczenie rozmaitych przypadków konkretnych zastosowań danego języka. Jeśli zetknąłeś się już kiedyś z Javą, dobrze znasz jej podstawy, lecz zależy Ci na opanowaniu szerszego spektrum możliwości, powinieneś koniecznie sięgnąć po książkę "Java. Ćwiczenia zaawansowane. Wydanie II". Znajdziesz tu zestaw niemal stu ćwiczeń pomagających zrozumieć takie zagadnienia, jak programowanie współbieżne i obsługa wątków, budowanie aplikacji wielowątkowych z interfejsem graficznym, nawiązywanie połączeń sieciowych, komunikacja sieciowa z użyciem protokołu wymiany danych, serwery wielowątkowe, współpraca z relacyjnymi bazami danych czy obsługa zapytań SQL. Nauczysz się uruchamiać, przerywać i synchronizować wątki oraz poznasz sposób działania gniazd w Javie i gniazd serwerowych. Dowiesz się więcej o przesyłaniu danych w sieci, serwerach wielowątkowych i sterowaniu serwerem z konsoli. Zobaczysz, jak powinno wyglądać budowanie aplikacji sieciowych z interfejsem graficznym w Javie oraz o czym koniecznie trzeba pamiętać. Ponadto jasne staną się dla Ciebie wszystkie tajniki komunikacji z bazą danych oraz sposób obsługi zapytań w SQL. Programowanie współbieżne Synchronizacja wątków Programowanie sieciowe Transmisja danych w modelu klient-serwer Aplikacje sieciowe z interfejsem graficznym Współpraca z bazami danych Błyskawicznie opanuj nowe umiejętności i zaimponuj klientom!

Java Data Analysis. Data mining, big data analysis, NoSQL, and data visualization

John R. Hubbard

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the aim of discovering useful information. Java is one of the most popular languages to perform your data analysis tasks.This book will help you learn the tools and techniques in Java to conduct data analysis without any hassle. After getting a quick overview of what data science is and the steps involved in the process, you’ll learn the statistical data analysis techniques and implement them using the popular Java APIs and libraries. Through practical examples, you will also learn the machine learning concepts such as classification and regression. In the process, you’ll familiarize yourself with tools such as Rapidminer and WEKA and see how these Java-based tools can be used effectively for analysis. You will also learn how to analyze text and other types of multimedia. Learn to work with relational, NoSQL, and time-series data. This book will also show you how you can utilize different Java-based libraries to create insightful and easy to understand plots and graphs.By the end of this book, you will have a solid understanding of the various data analysis techniques, and how to implement them using Java.

Java Data Science Cookbook. Explore the power of MLlib, DL4j, Weka, and more

Rushdi Shams

If you are looking to build data sciencemodels that are good for production,Java has come to the rescue. With the aidof strong libraries such as MLlib, Weka,DL4j, and more, you can efficientlyperform all the data science tasks youneed to.This unique book provides modernrecipes to solve your common andnot-so-common data science-relatedproblems. We start with recipes to helpyou obtain, clean, index, and search data.Then you will learn a variety of techniquesto analyze, learn from, and retrieveinformation from data. You will alsounderstand how to handle big data, learndeeply from data, and visualize data.Finally, you will work through uniquerecipes that solve your problems whiletaking data science to production, writingdistributed data science applications,and much more - things that will come inhandy at work.

Java: Data Science Made Easy. Data collection, processing, analysis, and more

Richard M. Reese, Jennifer L. Reese, Alexey...

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

Java Deep Learning Cookbook. Train neural networks for classification, NLP, and reinforcement learning using Deeplearning4j

Rahul Raj

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.

Java Deep Learning Essentials. Unlocking the next generation of predictive power

Yusuke Sugomori

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!

Java Deep Learning Projects. Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs

Md. Rezaul Karim

Java is one of the most widely used programming languages. With the rise of deep learning, it has become a popular choice of tool among data scientists and machine learning experts.Java Deep Learning Projects starts with an overview of deep learning concepts and then delves into advanced projects. You will see how to build several projects using different deep neural network architectures such as multilayer perceptrons, Deep Belief Networks, CNN, LSTM, and Factorization Machines.You will get acquainted with popular deep and machine learning libraries for Java such as Deeplearning4j, Spark ML, and RankSys and you’ll be able to use their features to build and deploy projects on distributed computing environments.You will then explore advanced domains such as transfer learning and deep reinforcement learning using the Java ecosystem, covering various real-world domains such as healthcare, NLP, image classification, and multimedia analytics with an easy-to-follow approach. Expert reviews and tips will follow every project to give you insights and hacks.By the end of this book, you will have stepped up your expertise when it comes to deep learning in Java, taking it beyond theory and be able to build your own advanced deep learning systems.

Java dla bystrzaków. Wydanie VII

Barry A. Burd

W prostocie tkwi siła Używaj popularnych narzędzi Javy Twórz proste obiekty Javy i ponownie używaj kodu Obsługuj zdarzenia i wyjątki Szybko naucz się Javy! Java jest wszędzie, a rosnący popyt na aplikacje WWW i dla Androida sprawia, że programiści Javy są poszukiwani bardziej niż kiedykolwiek! Ten świetny podręcznik opisuje najważniejsze elementy tego języka, takie jak powłoka JShell. W książce znajdziesz też praktyczne pytania i ćwiczenia, które pomogą Ci rozwinąć umiejętności programowania w Javie. Dzięki prostym instrukcjom obsługiwania klas i metod języka Java, stosowania zmiennych oraz sterowania przepływem programu szybko staniesz się ekspertem programowania w Javie! W książce: Podstawowe elementy Javy Wszystko o powłoce JShell Wskazówki dotyczące pętli Używanie klas i obiektów Żonglowanie wartościami w tablicach Obsługa interfejsów w Javie Zarządzanie klasami abstrakcyjnymi Dziesięć metod unikania błędów