Unilibrary. Oliy ta'lim muassasalarining yagona elektron kutubxona axborot tizimi.

Java Challengers

Java is one of the most used programming languages in the world, and it's also very powerful. Developers who know Java in depth are highly valued by the market and able to create high-quality, maintainable code. Java is not a simple language to master, but this book will provide valuable guidance for developers who want to harness its full power and be a reference as a software engineer. Even senior developers may not be familiar with some key concepts of the Java language, and that will impact the quality of the code they write. But reading a book is not enough to really acquire a skill. Developers have to put their knowledge into practice by applying what they've learned in some way. This book provides at least 5 Java Code Challenges per chapter to test your understanding of the and trully absorb the concepts it introduces. You'll get up-to-date with the most recent Java versions and will learn Java from real project situations so that when you come across similar situations you will know how to respond effectively.

McGraw-Hill Education TOEFL iBT: 3 Practice Tests

McGraw-Hill's TOEFL will help you reach the exam score you want. Inside you will find a full-scale test-prep program that combines book, interactive CD-ROM, and online audio to give you the most complete TOEFL instruction and practice available anywhere.

NEFT VA GAZ KONLARI GEOLOGIYASI

Darslikda tog’ jinslarining va qatlam flyuidlarining fizik va kimyoviy xususiyatlari, ularning neft va gaz uyumlarining hosil bo’lishidagi ahamiyati, quduqlarni burg’ilash, kern va shlam olish, geologik-geofizik malumotlarni qayta ishlash, qatlamni ochish va quduqni teshish metodlari berilgan.

Introduction to the Math of Neural Networks

This book introduces the reader to the basic math used for neural network calculation. This book assumes the reader has only knowledge of college algebra and computer programming. This book begins by showing how to calculate output of a neural network and moves on to more advanced training methods such as backpropagation, resilient propagation and Levenberg Marquardt optimization. The mathematics needed by these techniques is also introduced. Mathematical topics covered by this book include first, second, Hessian matrices, gradient descent and partial derivatives. All mathematical notation introduced is explained. Neural networks covered include the feedforward neural network and the self organizing map. This book provides an ideal supplement to our other neural books. This book is ideal for the reader, without a formal mathematical background, that seeks a more mathematical description of neural networks.

AVTOTRANSPORT VOSITALARINI DIAGNOSTIKALASH

Darslik О'zbekiston Respublikasi oliy va о rta maxsus ta 'lim vazirligi tomonidan tavsiya etilgan bo lib, oliy o'quvyurtlarining mexanika 5310600-Yer usti transport tizimlari ш ularning ekspluatasiyasi (avtomobil transporti), 5313100 Avtomobil transporti, yo'l qurilish mashinalari va jihozlarining ekspluatasiyasi (avtomobil transporti bo 'yicha), 5610100 - Xizmatlar sohasi (avtomobil transporti) la ’lim yo nalishlari va boshqa yo nalishlari bo yicha oliy о quv yunlarida ta ’lim olayotgan bakalavriat va professional ta ’lim muassasalari talabalari, magistrantlar, doktarantlar, о qituvchilar, avtotransport va avtoservis korxonalari muhandis-texnik xodimlariga mo'jallangan.

Адабиёт уқитиш методикаси

Мазкур «Адабиёт укитиш методикаси» дарслиги амалдаги укув режаси ва шу режа асосидаги укув дастури асосида яратилган. Унда адабиёт укитишнинг энг долзарб муаммолари бугунги кун талаблари нуктаи назаридан ёритиб берилган. Укув куллаимаси педагогика университетлари ва институтларининг филология факультетлари талабалари учун мулжалланган

ANALITIK KIMYO

Ushbu darslik oliy o'quv yurtlarining kimyodan boshqa ixtisosga o'qiydigan tallabalari uchun mo'ljallangan lekin unda o'rta maxsus o'quv yurtlarining talabalari hamda kimyoviy analiz bilan shug'ullannuvchilar bilan foydalanishhlari mumkin

VETERINARIYA GENETIKASI VA URCHITISH ASOSLARI

Irsiyat turlari. Irsiyat va o‘zgaruvchanlik har bir organizmning asosiy xususiyatlaridan hisoblanadi. Irsiyat va o‘zgaruvchanlikni o'rganishda avvalambor organizm bir butunligicha o‘rganilmasdan, balki uning ayrim belgilari va xususiyatlari o‘rganiladi. Genetikada belgi va xususiyatlar asosiy tushunchalar hisoblanadi. Belgi va xususiyat bular shartli o‘lchov bo‘lib, organizmning morfologik, fiziologik va bioximik xususiyatlarini ko‘rsatadi. Har bir tur, zot yoki o‘simlik navi o‘ziga xos irsiy belgi va xususiyatga egadir, u bulami keyingi avlodlarga o‘tkazishga harakat qiladi. Hayotning ma’lum bir davrlarida hayvonlarda va o'simliklarda shu zot yoki nav uchun xos bo‘lgan belgi va xususiyatlar shakllanadi va ular tartibli ravishda keyingi avlodlarda namoyon bo‘lib tur, zot, navning xususiyatlrini saqlab qoladi.

ИСЛАМСКОЕ ПРАВО

В учебнике рассматриваются теоретические проблемы и современная практика исламского права. Основу книги составляет анализ главных разделов канонического исламского права, положений, регулирующих торговлю и финансы, налоговую систему; изучает законы о браке и семье, запреты и наказания, вопросы собственности, статуса личности и прав мусульманина, систему судопроизводства. Предназначен для бакалавров и магистров юридических вузов, слушателей и адъюнктов Академии МВД, а также для тех, кто интересуется вопросами исламского права. Авторы будут благодарны всем тем, кто направит свои замечания и предложения по дальнейшему совершенствованию данного учебника.

Адабиётшунослик асослари.

Мазкуркитоб муаллифнинг "Укитувчи" нашриёти томонидан 1979 йилда чоп этилган "Адабиётшуносликка кириш курси буйича укув-методик кулланма"сининг кайта ишланган ва тулдирилган иккинчи нашридир.

Чет эл адабиёти тарихи.

Урта асрлар адабиёти антик кулдорлик жамияти урнида вужудга келган янги ижтимоий тузум - феодализм негизида шаклланади.

Introduction to Deep Learning

It is standard to start exploring deep learning (or neural nets — we use the terms interchangeably) with their use in computer vision. This area of artificial intelligence has been revolutionized by the technique and its basic starting point — light intensity — is represented naturally by real numbers, which are what neural nets manipulate. To make this more concrete, consider the problem of identifying handwritten digits — the numbers from zero to nine.

Узбек адабиёти тарихи Беш томлик.

Алишер Навоийнинг хаёти ва ижоди, унинг хаёти ва фаолияти билан боглик масалалар олимлар томонидан илмий асосларда кенг ва чукур урганилади.

Inside the Machine. An Illustrated Introduction to Microprocessors and Computer Architecture

The book's comparative approach, described below, introduces new design features by comparing them with earlier features intended to solve the same problem(s). Thus, beginning and intermediate readers are encouraged to read the chapters in order, because each chapter assumes a familiarity with the concepts and processor designs introduced in the chapters prior to it. Inside the Machine is an introduction to computers that is intended to fill the gap that exists between classic but more challenging introductions to computer architecture, like John L. Hennessy's and David A. Patterson's popular textbooks, and the growing mass of works that are simply too basic for motivated non-specialist readers.

Детские пьесы для фортепиано

Заманчивый мир юношеских порывов и мечтаний сказочных героев и пестрых сюжетов действительности нашел богатое и многостороннее отражение в болгарской музыкальной литературе предназначенной для молодежи.

Graph-Powered Analytics and Machine Learning with TigerGraph

The goal of this book is to introduce you to the concepts, techniques, and tools for graph data structures, graph analytics, and graph machine learning. When you've finished the book, we hope you'll understand how graph analytics can be used to address a range of real-world problems. We want you to be able to answer questions like the following: Is graph a good fit for this task? What tools and techniques should I use? What are the meaningful relationships in my data, and how do I formulate a task in terms of relationship analysis. In our experience, we see that many people quickly grasp the general concept and structure of graphs, but it takes more effort and experience to “think graph,” that is, to develop the intuition for how best to model your data as a graph and then to formulate an analytical task as a graph query.