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Deep Learning for Natural Language Processing : Solve Your Natural Language Processing Problems with Smart Deep Neural Networks
Deep Learning for Natural Language Processing : Solve Your Natural Language Processing Problems with Smart Deep Neural Networks
"Gain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues. Key Features Gain insights into the basic building blocks of natural language processing Learn how to select the best deep neural network to solve your NLP problems Explore convolutional and recurrent neural networks and long short-term memory networks Book Description Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you'll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search. By the end of this book, you will not only have sound knowledge of natural language processing but also be able to select the best text pre-processing and neural network models to solve a number of NLP issues. What you will learn Understand various pre-processing techniques for deep learning problems Build a vector representation of text using word2vec and GloVe Create a named entity recognizer and parts-of-speech tagger with Apache OpenNLP Build a machine translation model in Keras Develop a text generation application using LSTM Build a trigger word detection application using an attention model Who this book is for If you're an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must. "
Natural Language Processing with Python Quick Start Guide : Going from a Python Developer to an Effective Natural Language Processing Engineer
Natural Language Processing with Python Quick Start Guide : Going from a Python Developer to an Effective Natural Language Processing Engineer
Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learning Key Features A no-math, code-driven programmer's guide to text processing and NLP Get state of the art results with modern tooling across linguistics, text vectors and machine learning Fundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorch Book Description NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a work?ow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges. What you will learn Understand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpus Work with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clustering Deep Learning in NLP using PyTorch with a code-driven introduction to PyTorch Using an NLP project management Framework for estimating timelines and organizing your project into stages Hack and build a simple chatbot application in 30 minutes Deploy an NLP or machine learning application using Flask as RESTFUL APIs Who this book is for Programmers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory.
Advanced Deep Learning with R : Become an Expert at Designing, Building, and Improving Advanced Neural Network Models Using R
Advanced Deep Learning with R : Become an Expert at Designing, Building, and Improving Advanced Neural Network Models Using R
Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R libraries Key Features Implement deep learning algorithms to build AI models with the help of tips and tricks Understand how deep learning models operate using expert techniques Apply reinforcement learning, computer vision, GANs, and NLP using a range of datasets Book Description Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples. What you will learn Learn how to create binary and multi-class deep neural network models Implement GANs for generating new images Create autoencoder neural networks for image dimension reduction, image de-noising and image correction Implement deep neural networks for performing efficient text classification Learn to define a recurrent convolutional network model for classification in Keras Explore best practices and tips for performance optimization of various deep learning models Who this book is for This book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to develop their skills and knowledge to implement deep learning techniques and algorithms using the power of R. A solid understanding of machine learning and working knowledge of the R programming language are required.
Building Chatbots with Python : Using Natural Language Processing and Machine Learning
Building Chatbots with Python : Using Natural Language Processing and Machine Learning
Build your own chatbot using Python and open source tools. This book begins with an introduction to chatbots where you will gain vital information on their architecture. You will then dive straight into natural language processing with the natural language toolkit (NLTK) for building a custom language processing platform for your chatbot. With this foundation, you will take a look at different natural language processing techniques so that you can choose the right one for you. The next stage is to learn to build a chatbot using the API.ai platform and define its intents and entities. During this example, you will learn to enable communication with your bot and also take a look at key points of its integration and deployment. The final chapter of Building Chatbots with Python teaches you how to build, train, and deploy your very own chatbot. Using open source libraries and machine learning techniques you will learn to predict conditions for your bot and develop a conversational agent as a web application. Finally you will deploy your chatbot on your own server with AWS. What You Will Learn Gain the basics of natural language processing using Python Collect data and train your data for the chatbot Build your chatbot from scratch as a web app Integrate your chatbots with Facebook, Slack, and Telegram Deploy chatbots on your own server Who This Book Is For Intermediate Python developers who have no idea about chatbots. Developers with basic Python programming knowledge can also take advantage of the book.
Deep Learning for Natural Language Processing : Solve Your Natural Language Processing Problems with Smart Deep Neural Networks
Deep Learning for Natural Language Processing : Solve Your Natural Language Processing Problems with Smart Deep Neural Networks
"Gain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues. Key Features Gain insights into the basic building blocks of natural language processing Learn how to select the best deep neural network to solve your NLP problems Explore convolutional and recurrent neural networks and long short-term memory networks Book Description Applying deep learning approaches to various NLP tasks can take your computational algorithms to a completely new level in terms of speed and accuracy. Deep Learning for Natural Language Processing starts off by highlighting the basic building blocks of the natural language processing domain. The book goes on to introduce the problems that you can solve using state-of-the-art neural network models. After this, delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs. As you advance through this deep learning book, you'll study convolutional, recurrent, and recursive neural networks, in addition to covering long short-term memory networks (LSTM). Understanding these networks will help you to implement their models using Keras. In the later chapters, you will be able to develop a trigger word detection application using NLP techniques such as attention model and beam search. By the end of this book, you will not only have sound knowledge of natural language processing but also be able to select the best text pre-processing and neural network models to solve a number of NLP issues. What you will learn Understand various pre-processing techniques for deep learning problems Build a vector representation of text using word2vec and GloVe Create a named entity recognizer and parts-of-speech tagger with Apache OpenNLP Build a machine translation model in Keras Develop a text generation application using LSTM Build a trigger word detection application using an attention model Who this book is for If you're an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the book for you. Strong working knowledge of Python, linear algebra, and machine learning is a must. "
Natural Language Processing with Python Quick Start Guide : Going from a Python Developer to an Effective Natural Language Processing Engineer
Natural Language Processing with Python Quick Start Guide : Going from a Python Developer to an Effective Natural Language Processing Engineer
Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learning Key Features A no-math, code-driven programmer's guide to text processing and NLP Get state of the art results with modern tooling across linguistics, text vectors and machine learning Fundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorch Book Description NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a work?ow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges. What you will learn Understand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpus Work with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clustering Deep Learning in NLP using PyTorch with a code-driven introduction to PyTorch Using an NLP project management Framework for estimating timelines and organizing your project into stages Hack and build a simple chatbot application in 30 minutes Deploy an NLP or machine learning application using Flask as RESTFUL APIs Who this book is for Programmers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory.
Advanced Deep Learning with R : Become an Expert at Designing, Building, and Improving Advanced Neural Network Models Using R
Advanced Deep Learning with R : Become an Expert at Designing, Building, and Improving Advanced Neural Network Models Using R
Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R libraries Key Features Implement deep learning algorithms to build AI models with the help of tips and tricks Understand how deep learning models operate using expert techniques Apply reinforcement learning, computer vision, GANs, and NLP using a range of datasets Book Description Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples. What you will learn Learn how to create binary and multi-class deep neural network models Implement GANs for generating new images Create autoencoder neural networks for image dimension reduction, image de-noising and image correction Implement deep neural networks for performing efficient text classification Learn to define a recurrent convolutional network model for classification in Keras Explore best practices and tips for performance optimization of various deep learning models Who this book is for This book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to develop their skills and knowledge to implement deep learning techniques and algorithms using the power of R. A solid understanding of machine learning and working knowledge of the R programming language are required.
Building Chatbots with Python : Using Natural Language Processing and Machine Learning
Building Chatbots with Python : Using Natural Language Processing and Machine Learning
Build your own chatbot using Python and open source tools. This book begins with an introduction to chatbots where you will gain vital information on their architecture. You will then dive straight into natural language processing with the natural language toolkit (NLTK) for building a custom language processing platform for your chatbot. With this foundation, you will take a look at different natural language processing techniques so that you can choose the right one for you. The next stage is to learn to build a chatbot using the API.ai platform and define its intents and entities. During this example, you will learn to enable communication with your bot and also take a look at key points of its integration and deployment. The final chapter of Building Chatbots with Python teaches you how to build, train, and deploy your very own chatbot. Using open source libraries and machine learning techniques you will learn to predict conditions for your bot and develop a conversational agent as a web application. Finally you will deploy your chatbot on your own server with AWS. What You Will Learn Gain the basics of natural language processing using Python Collect data and train your data for the chatbot Build your chatbot from scratch as a web app Integrate your chatbots with Facebook, Slack, and Telegram Deploy chatbots on your own server Who This Book Is For Intermediate Python developers who have no idea about chatbots. Developers with basic Python programming knowledge can also take advantage of the book.

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OPACOPAC
本学図書館の所蔵資料検索データベースです。
CiNii ArticlesCiNii Articles
国立情報学研究所(NII)が提供する、論文や図書・雑誌などの学術情報を、論文タイトルや収録雑誌名などから検索できるデータベースです。
Webcat PlusWebcat Plus
国立情報学研究所 (NII)が提供する、全国の大学図書館や国立国会図書館の所蔵目録、電子ブックデータベースなど、本に関する様々な情報源を統合して検索することができるサービスです。
通常の検索だけでなく、入力した言葉から連想して検索する「連想検索」で検索することもできます。
JDreamIIIJDreamIII
国内最大の理工系データベース。60数カ国の学術雑誌論文を網羅。「文献速報」のオンライン版。終了は、必ず「ログアウト」をクリック。
Google ScholarGoogle Scholar
分野や発行元を問わず、学術出版社、専門学会、プレプリント管理機関、大学、およびその他の学術団体の学術専門誌、論文、書籍、要約、記事を検索できます。
IRDB(学術機関リポジトリデータベース)IRDB(学術機関リポジトリデータベース)
国立情報学研究所(NII)が提供する、日本の学術機関リポジトリに蓄積された大学や研究機関の学術情報(学術雑誌論文、学位論文、研究紀要、研究報告書、教材など)を横断的に検索できるサービスです。
医中誌Web医中誌Web
国内の医学、薬学、歯学及び周辺領域(看護学、獣医学等)の論文情報を網羅したデータベース。
約4,700誌の定期刊行物から年間30万件以上の文献論文情報を収録。
ScopusScopus
科学・技術・医学(STM)文献の抄録・引用・検索においても世界最大のコレクションを網羅。抄録の最も古いものは1966年まで。
Web of ScienceWeb of Science
クラリベイト・アナリティクス社が提供する自然科学系の学術誌の中から、特に影響力の高い雑誌に収録されている論文について、論文情報、引用情報が検索できるデータベースです。
※同時にアクセスできる人数に制限があるため、ご利用ができない場合がありますので、ご了承ください。また、終了する際は必ず「Lo>※JCR(inCites Journal Citation Reports)が利用できるようになりました。JCRとは 論文の引用データベースを使って、学術雑誌の影響度を評価・比較するツールです。
※Web of Scienceの一番上のタブJournal Citation Reportsをクリックし、ご利用ください。
Sci Finder(Web版)Sci Finder(Web版)(ログイン/新規登録)
化学情報協会が提供する、化学、物理、生物分野を中心に、3000万件を越える雑誌論文、特許情報の他、化学物質情報や化学反応情報などが検索できるデータベースです。
キーワード検索だけでなく、化学構造式や化学式の一部からも検索ができます。
ご利用にはアカウント登録が必要です。
アカウント登録は左の「新規登録」から行ってください。
MathSciNetMathSciNet
アメリカ数学会(American Mathematical Society)が提供する。1940年以降の世界の数学関係文献を包括するデータベース。
LE CORBUSIER PLANSLE CORBUSIER PLANS (ログイン/ユーザー登録)
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Echelle-1が提供する建築家ル・コルビュジエが建築に関わった作品のスケッチ、設計図面など、35,000点を超える資料を収録したデータベースです。
プロジェクト名からの検索だけでなく、キーワードからの検索もできます。
ご利用にはユーザー登録が必要となります。
朝日新聞記事データベース朝日新聞記事データベース
朝日新聞記事データベース「聞蔵」のバージョンアップ版。1945年以降から当日の朝刊記事までの記事が検索可能。1945年~1984年は戦後紙面イメージで,1984年~当日分はテキスト+切り抜きイメージを表示。沖縄を除く各都道府県の県庁所在地を含んだ地方版,および朝日新聞社のニュース週刊誌「AERA(1988/5以降)」,「週刊朝日(2000/4以降)」,「知恵蔵最新版」も収録。検索終了後は必ず『ログアウト』をクリック。
日経Value Search日経テレコン
日本経済新聞をはじめとした、日経各紙に掲載された記事や、速報ニュースが検索可能。
また、企業・業界分析に必要な情報もシ収集できる就職活動にも有用なデータベース。検索終了後は必ず「ログアウト」をクリック。
ヨミダス歴史館ヨミダス歴史館
読売新聞本紙・地方紙・英字新聞「THE DAILY YOMIURI」の記事検索が可能。検索終了後は必ず「ログアウト」をクリック。
東洋経済デジタルコンテンツ・ライブラリー東洋経済デジタルコンテンツ・ライブラリー
東洋経済の発行する雑誌13誌「週刊東洋経済」「業界地図」「金融ビジネス」「一橋ビジネスレビュー」「ThinK!」「オール投資」「統計月報」の他、企業研究のバイブル「会社四季報」、独自で企業調査を行った「就職四季報」など25誌が閲覧可能。
学術研究用のレポート・論文作成や、就職活動に役立つ情報を収録しており、最新の経済事情や企業情報をフリーワードで検索できます。
eol(企業情報データベース)eol(企業情報データベース)
有価証券報告書や決算短信・取引所開示書類をはじめとした膨大な企業情報を、さまざまな角度から配信するデータベースサービス。企業属性情報、マーケット情報、財務情報、原文データの4つのコンテンツとそれを取り出す各種検索、比較・分析、ダウンロードの3つの機能から構成。就職活動に役立つ情報を掲載。
Japan KnowledgeJapan Knowledge
多数の百科事典を一度に検索できます。「言葉」から現在出版されている図書も検索可能。検索終了後は、必ず「ログアウト」をクリック。
日経BP記事検索サービス日経BP記事検索サービス
日経BP社が発行する雑誌50タイトル以上のバックナンバー記事をオンライン上でダウンロード可能。※契約閲覧本数の上限を超えると利用できなくなります。必要な記事のみ閲覧をお願いいたします。なお、異常な本数の閲覧は管理されております。
J-STAGEJ-STAGE
自然科学系の国内学会誌の検索・閲覧
LibrariELibrariE
幅広い分野を収録した電子図書館サービスです。スマートフォン・タブレット・自宅のパソコンから閲覧・貸出ができます。
ログイン方法等についてはこちら(※学内限定)をご覧ください。
丸善eBook Library丸善eBook Library
丸善が提供する、学術情報に特化した電子ブックを提供するサービスです。
本学でも貸出が多い「建築設計資料」シリーズを始め、理工学の専門書を中心に約700冊の図書を読むことができます。
読むことができるタイトルはこちら
EBSCOhost eBook CollectionEBSCOhost eBook Collection
本学で契約している電子ブックコレクションです。約4000冊を読むことができます。
ProQuest Ebook CentralProQuest Ebook Central
ProQuest社が提供する幅広い分野を収録した電子ブックコレクション。
図書館イベント「電子ブック選書会」で選ばれた電子ブックを読むことができます。「電子ブック選書会」についてはこちら
Science DirectScience Direct (Freedom Collection)
Elsevier社が提供する世界最大のフルテキストデータベースです。
Freedom Collectionでは、Elsevier社が発行する約2,000誌の科学・技術・医学・社会科学分野の雑誌について、概ね1995年以降の学術論文を読むことができます。
Wiley Online eBooksWiley Online eBooks
Wiley-Blackwell社が発行する雑誌のうち、19タイトルの雑誌について、1997年以降の記事が検索できます。
Advanced Materials“、”Angewandte Chemie International Edition“、”Earthquake Engineering & Structural Dynamics“は、創刊号から読むことができます。 はFullTextを読むことができます。
NatureNature
「Nature」のオンライン版。1997年から全文閲覧可能。
IEEEIEEE Xplore(IEEE(アイトリプルイー)IET(英国工学技術学会))
IEEE(アイトリプルイー)とIET(英国工学技術学会)の刊行物の大部分を収録。世界中の電気・電子・コンピュータ系を網羅。収録年は、1988年~。注意事項:ロボットアタックなどのプログラムを構築して大量のコンテンツを一度にダウンロードする事は厳禁。
Springer LinkSpringer Link
Springer社が発行する約1600タイトルの雑誌について、記事が検索できます。
検索結果の左上にある「Include preview-only content」のチェックを外すと、本文を読むことができる記事のみ検索ができます。