就職活動を行なう際に、まず手をつけなければいけない業界研究。

筆記試験対策として「一般常識」の問題・設問に対応。

自身の特徴、個性、能力などの整理から、適職診断、業界、業種と自己をつなげる志望動機への発展までを具体的なWork（ワークシートへの書き込み作業）を元に進めていくことが出来ます。

就職アドバイザー・才木弓加氏が、就職採用試験における面接対策を伝授！

面接試験をクリアするためのポイント、それは…。「面接は聞かれたことを答える場ではない!」

就職アドバイザー・岡茂信氏が、内定を引き寄せるためのエントリーシート作成術を伝授！

「インターンシップ・仕事体験」のすべてを紹介する書籍。

忙しい就活生のための、SPI対策問題集。

出題分野すべての解法パターンが出題頻度順に学習できるSPI3＆テストセンター対策の決定版！

就職アドバイザー・才木弓加氏伝授！Web面接の対策のため必要なのは、特徴を「理解」し、正しい「準備」を行うこと！

この面接は、嘘をつけない、脚色できない、準備できない。だから、学生の素顔がわかる！
成績表を活用する全く新しい採用手法、唯一の解説書。単なる「成績重視」に陥ることなく、学生の内面に迫る面接手法のすべてを、これ一冊で解説する。

アナリストランキング４年連続１位のみずほ証券が、企業分析のノウハウと情報を全面開示。中長期成長を実現する日本企業を読み解く。

身だしなみから、電話・メール・手紙、アルバイト・就職活動まで、大学生なら知っておきたい、社会人として常識的なマナーをシチュエーション別に紹介する。チェック問題、秘書技能検定試験3級の過去問題の抜粋も収録。

SNS全盛時代に企業の求める人材・スキルとは? できる人はFacebookをどう使っているのか? 情報を集めやすいFacebookとTwitterを取り上げ、就職活動、転職活動に役立つ情報を紹介する。

長時間労働や過労死の危機感は募り、働き方改革が急がれる。そもそも私たちは働きたくないのか？いや、「働きたいように、働きたい」のだ。モバイルメディアが普及した現代の「働き方」は、何がどう変わったのか、変わっていないのか。日本のみならず世界各地でのフィールドワークに基づき、働き方への新しい視点と可能性を提供する。

平成から令和へ。オリンピックを前に東京はいかに変貌したか？　近過去の建築景観、丹下健三以後の東京計画・未来都市像を検証する。

デザイン初心者でも、ロゴデザインでしっかり想いやイメージを表現できるよう、デザインの基本からアイデア出し、納品までのアイデアを65の引き出しとして紹介。見るだけで楽しくなる&参考になる作例を豊富に収録。

入学したその日から卒業まで使える、評価につながるライティングテクニック

生い立ちから現在までをたどりつつ、各種建築作品を精緻に読みとく。世界の「アンドー」の全体像に迫る本格評伝にして作品論の決定版。

プログラミングの基礎を履修済みの学生や研究者に向けて、脳波、心電図、筋電図、fNIRSの解析方法について丁寧に解説。ダウンロード可能なサンプルプログラムで実践的にMATLABプログラミングの基礎が習得できる一冊。

ラジオ録音、音楽プレイヤー、水温センサー、監視カメラ、簡易電力計、テレビの録画……Raspberry Piで作る実践的なガジェットを解説。難易度も、ソフトウェアの設定で終わる★1つの簡単なものから、はんだ作業やプログラムが大変な★★★★★のものまで、色々と用意しました。ぜひ自分の技術にあったもの、作って使ってみたいものを選択し、挑戦してみてください。

車両運動力学の入門テキスト。基礎となる一般力学、機械力学、振動学等の知識を前提に、車両の運動と制御の問題を理論的に扱う。最新データを取り入れ、例題や問題を充実させた第2版。

試行錯誤しながら実際に２棟の「３００年住宅」をつくった福永博建築研究所が、そこから導き出された「長命化」の方向性について、３つの軸と９つの指標をあげて提案する。

ベストセラーの「キクタン」シリーズに理系英語版が登場！　長くて複雑な発音の理系用語も、チャンツのリズムに乗って楽しく覚えられます。「工学」を扱う本書では、各見出し語に分かりやすい解説を付けるとともに、必要に応じて例文や図解を掲載しています。学習した英語表現が指す物や概念についてあらためて確認することにより、専門分野の知識の整理・定着まで行うことができます。（付属のCD-ROMは電子書籍には収録されていません）

イラスト解説で楽しくわかる。実力派スクリプト言語のエッセンスを素早くマスター。

伝統的な日本の建築物に多く使われる、木造ならではの独特のデザイン・文様を集めた建築図案集。

"発売から8年であっという間に総計40万部到達した大人気シリーズの原点。 「どうして？」「なぜそうなる？」が必ずわかるJava入門書史上最強の定番書の増補改訂版登場！ 従来の入門書は、先生が生徒が一方的に話し続けるようなスタイルで構成されているモノが多く、それらは途中初学者が思い浮かべる「なぜ？」「どうして？」を置き去りにしたまま次の解説に移っていくため、理解が進まず多くの挫折を生み出してきました。 本書は、初学者がよくつまずくポイントや疑問点に対して、いろいろな角度から具体的なアドバイスを行っているため、まだ腑に落ちていないのに勝手にストーリーが進むということがなく、一歩一歩着実に理解を深めて、無駄なく短期間で知識を習得できる構成になっています。"

"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. "

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.

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.

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.

Fluid mechanics is often seen as the most difficult core subject encountered by engineering students. The problem stems from the necessity to visualise complex flow patterns and fluid behaviour modelled by high level mathematics. This text overcomes this difficulty by introducing the concepts through everyday examples, before moving on to the more involved mathematics. The various theories of flow have been correlated with real phenomena and, combined with numerous figures and photographs, help the reader place the subject in context. Examples from a broad range of engineering disciplines are included making this textbook suitable for all engineers studying fluid systems as part of their degree. Introduction to Fluid Mechanics is translated from the best-selling Japanese book by Professor Yasuki Nakayama, and adapted for the international market by Professor Robert Boucher. Introduces the concepts through everyday examples before moving on to the more invoved mathematics Various theories of flow are applied to real phenomena and illustrated with numerous figures and photographs Includes examples from a bread range of engineering disciplines

This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field. This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.