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1位
¥3,738 円
評価: 0
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楽天Kobo電子書籍ストア
This book is about 24 developing countries that have embarked on the journey towards universal health coverage (UHC) following a bottom-up approach, with a special focus on the poor and vulnerable, through a systematic data collection that provides practical insights to policymakers and practitioners. Each of the UHC programs analyzed in this book is seeking to overcome the legacy of inequality by tackling both a “financing gap†? and a “provision gap†?: the financing gap (or lower per capita spending on the poor) by spending additional resources in a pro-poor way; the provision gap (or underperformance of service delivery for the poor) by expanding supply and changing incentives in a variety of ways. The prevailing view seems to indicate that UHC require not just more money, but also a focus on changing the rules of the game for spending health system resources. The book does not attempt to identify best practices, but rather aims to help policy makers understand the options they face, and help develop a new operational research agenda. The main chapters are focused on providing a granular understanding of policy design, while the appendixes offer a systematic review of the literature attempting to evaluate UHC program impact on access to services, on financial protection, and on health outcomes.画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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2位
¥4,993 円
評価: 0
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楽天Kobo電子書籍ストア
＜p＞＜strong＞Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.＜/strong＞＜/p＞ ＜p＞＜strong＞Purchase of the print or Kindle book includes a free eBook in the PDF format.＜/strong＞＜/p＞ ＜h4＞Key Features＜/h4＞ ＜ul＞ ＜li＞Third edition of the bestselling, widely acclaimed Python machine learning book＜/li＞ ＜li＞Clear and intuitive explanations take you deep into the theory and practice of Python machine learning＜/li＞ ＜li＞Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices＜/li＞ ＜/ul＞ ＜h4＞Book Description＜/h4＞ ＜p＞Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.＜/p＞ ＜p＞Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.＜/p＞ ＜p＞Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.＜/p＞ ＜p＞This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.＜/p＞ ＜h4＞What you will learn＜/h4＞ ＜ul＞ ＜li＞Master the frameworks, models, and techniques that enable machines to 'learn' from data＜/li＞ ＜li＞Use scikit-learn for machine learning and TensorFlow for deep learning＜/li＞ ＜li＞Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more＜/li＞ ＜li＞Build and train neural networks, GANs, and other models＜/li＞ ＜li＞Discover best practices for evaluating and tuning models＜/li＞ ＜li＞Predict continuous target outcomes using regression analysis＜/li＞ ＜li＞Dig deeper into textual and social media data using sentiment analysis＜/li＞ ＜/ul＞ ＜h4＞Who this book is for＜/h4＞ ＜p＞If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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3位
¥3,290 円
評価: 0
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楽天Kobo電子書籍ストア
＜p＞＜strong＞Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras＜/strong＞＜/p＞ ＜h4＞Key Features＜/h4＞ ＜ul＞ ＜li＞Explore the most advanced deep learning techniques that drive modern AI results＜/li＞ ＜li＞New coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentation＜/li＞ ＜li＞Completely updated for TensorFlow 2.x＜/li＞ ＜/ul＞ ＜h4＞Book Description＜/h4＞ ＜p＞Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects.＜/p＞ ＜p＞Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques.＜/p＞ ＜p＞Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance.＜/p＞ ＜p＞Next, you'll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.＜/p＞ ＜h4＞What you will learn＜/h4＞ ＜ul＞ ＜li＞Use mutual information maximization techniques to perform unsupervised learning＜/li＞ ＜li＞Use segmentation to identify the pixel-wise class of each object in an image＜/li＞ ＜li＞Identify both the bounding box and class of objects in an image using object detection＜/li＞ ＜li＞Learn the building blocks for advanced techniques - MLPss, CNN, and RNNs＜/li＞ ＜li＞Understand deep neural networks - including ResNet and DenseNet＜/li＞ ＜li＞Understand and build autoregressive models ? autoencoders, VAEs, and GANs＜/li＞ ＜li＞Discover and implement deep reinforcement learning methods＜/li＞ ＜/ul＞ ＜h4＞Who this book is for＜/h4＞ ＜p＞This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be helpful. Knowledge of Keras or TensorFlow 2.0 is not required but is recommended.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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4位
¥1,508 円
評価: 0
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もったいない本舗 楽天市場店
著者：Danielle Steel出版社：Dellサイズ：その他ISBN-10：0440242061ISBN-13：9780440242062■こちらの商品もオススメです ● Johnny Angel / Danielle Steel / Dell [その他] ● The House on Hope Street / Danielle Steel / Dell [その他] ● Secrets / Danielle Steel / Dell [その他] ● Rogue / Danielle Steel / Dell [その他] ■通常24時間以内に出荷可能です。※繁忙期やセール等、ご注文数が多い日につきましては 発送まで48時間かかる場合があります。あらかじめご了承ください。 ■メール便は、1冊から送料無料です。※宅配便の場合、2,500円以上送料無料です。※あす楽ご希望の方は、宅配便をご選択下さい。※「代引き」ご希望の方は宅配便をご選択下さい。※配送番号付きのゆうパケットをご希望の場合は、追跡可能メール便（送料210円）をご選択ください。■ただいま、オリジナルカレンダーをプレゼントしております。■お急ぎの方は「もったいない本舗 お急ぎ便店」をご利用ください。最短翌日配送、手数料298円から■まとめ買いの方は「もったいない本舗 おまとめ店」がお買い得です。■中古品ではございますが、良好なコンディションです。決済は、クレジットカード、代引き等、各種決済方法がご利用可能です。■万が一品質に不備が有った場合は、返金対応。■クリーニング済み。■商品画像に「帯」が付いているものがありますが、中古品のため、実際の商品には付いていない場合がございます。■商品状態の表記につきまして・非常に良い： 使用されてはいますが、 非常にきれいな状態です。 書き込みや線引きはありません。・良い： 比較的綺麗な状態の商品です。 ページやカバーに欠品はありません。 文章を読むのに支障はありません。・可： 文章が問題なく読める状態の商品です。 マーカーやペンで書込があることがあります。 商品の痛みがある場合があります。
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5位
¥3,290 円
評価: 0
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楽天Kobo電子書籍ストア
＜p＞＜strong＞Fun and exciting projects to learn what artificial minds can create＜/strong＞＜/p＞ ＜h4＞Key Features＜/h4＞ ＜ul＞ ＜li＞Code examples are in TensorFlow 2, which make it easy for PyTorch users to follow along＜/li＞ ＜li＞Look inside the most famous deep generative models, from GPT to MuseGAN＜/li＞ ＜li＞Learn to build and adapt your own models in TensorFlow 2.x＜/li＞ ＜li＞Explore exciting, cutting-edge use cases for deep generative AI＜/li＞ ＜/ul＞ ＜h4＞Book Description＜/h4＞ ＜p＞Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI?＜/p＞ ＜p＞In this book, you'll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You'll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks.＜/p＞ ＜p＞There's been an explosion in potential use cases for generative models. You'll look at Open AI's news generator, deepfakes, and training deep learning agents to navigate a simulated environment.＜/p＞ ＜p＞Recreate the code that's under the hood and uncover surprising links between text, image, and music generation.＜/p＞ ＜h4＞What you will learn＜/h4＞ ＜ul＞ ＜li＞Export the code from GitHub into Google Colab to see how everything works for yourself＜/li＞ ＜li＞Compose music using LSTM models, simple GANs, and MuseGAN＜/li＞ ＜li＞Create deepfakes using facial landmarks, autoencoders, and pix2pix GAN＜/li＞ ＜li＞Learn how attention and transformers have changed NLP＜/li＞ ＜li＞Build several text generation pipelines based on LSTMs, BERT, and GPT-2＜/li＞ ＜li＞Implement paired and unpaired style transfer with networks like StyleGAN＜/li＞ ＜li＞Discover emerging applications of generative AI like folding proteins and creating videos from images＜/li＞ ＜/ul＞ ＜h4＞Who this book is for＜/h4＞ ＜p＞This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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6位
¥7,292 円
評価: 0
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楽天Kobo電子書籍ストア
＜p＞Machine learning has taken time to move into the space of academic economics. This is because empirical research in economics is concentrated on the identification of causal relationships in parsimonious statistical models; whereas machine learning is oriented towards prediction and is generally uninterested in either causality or parsimony. That leaves a gap for students, academics, and professionals who lack a standard reference on machine learning for economics and finance.＜/p＞ ＜p＞This book focuses on economic and financial problems with an empirical dimension, where machine learning methods may offer something of value. This includes coverage of a variety of discriminative deep learning models (DNNs, CNNs, LSTMs, and DQNs), generative machine learning models (GANs and VAEs), and tree-based models. It also covers the intersection of empirical methods in economics and machine learning, including regression analysis, natural language processing, and dimensionality reduction.＜/p＞ ＜p＞TensorFlow offers a toolset that can be used to define and solve any graph-based model, including those commonly used in economics. This book is structured to teach through a sequence of complete examples, each framed in terms of a specific economic problem of interest or topic. This simplifies otherwise complicated concepts, enabling the reader to solve workhorse theoretical models in economics and finance using TensorFlow.＜/p＞ ＜p＞＜strong＞What You'll Learn＜/strong＞＜/p＞ ＜ul＞ ＜li＞Define, train, and evaluate machine learning models in TensorFlow 2＜/li＞ ＜li＞Apply fundamental concepts in machine learning, such as deep learning and natural language processing, to economic and financial problems＜/li＞ ＜li＞Solve theoretical models in economics＜/li＞ ＜/ul＞ ＜p＞＜strong＞Who This Book Is For＜/strong＞＜/p＞ ＜p＞Students, data scientists working in economics and finance, public and private sector economists, and academic social scientists＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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7位
¥1,716 円
評価: 0
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楽天Kobo電子書籍ストア
＜p＞This easy-to-use reference for TensorFlow 2 design patterns in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow itself.＜/p＞ ＜p＞When and why would you feed training data as using NumPy or a streaming dataset? How would you set up cross-validations in the training process? How do you leverage a pretrained model using transfer learning? How do you perform hyperparameter tuning? Pick up this pocket reference and reduce the time you spend searching through options for your TensorFlow use cases.＜/p＞ ＜ul＞ ＜li＞Understand best practices in TensorFlow model patterns and ML workflows＜/li＞ ＜li＞Use code snippets as templates in building TensorFlow models and workflows＜/li＞ ＜li＞Save development time by integrating prebuilt models in TensorFlow Hub＜/li＞ ＜li＞Make informed design choices about data ingestion, training paradigms, model saving, and inferencing＜/li＞ ＜li＞Address common scenarios such as model design style, data ingestion workflow, model training, and tuning＜/li＞ ＜/ul＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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8位
¥6,685 円
評価: 0
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楽天Kobo電子書籍ストア
＜p＞Use TensorFlow 2.x with Google's Colaboratory (Colab) product that offers a free cloud service for Python programmers. Colab is especially well suited as a platform for TensorFlow 2.x deep learning applications. You will learn Colab’s default install of the most current TensorFlow 2.x along with Colab’s easy access to on-demand GPU hardware acceleration in the cloud for fast execution of deep learning models. This book offers you the opportunity to grasp deep learning in an applied manner with the only requirement being an Internet connection. Everything elseーPython, TensorFlow 2.x, GPU support, and Jupyter Notebooksーis provided and ready to go from Colab.＜/p＞ ＜p＞The book begins with an introduction to TensorFlow 2.x and the Google Colab cloud service. You will learn how to provision a workspace on Google Colab and build a simple neural network application. From there you will progress into TensorFlow datasets and building input pipelines in support of modeling and testing. You will find coverage of deep learning classification and regression, with clear code examples showing how to perform each of those functions. Advanced topics covered in the book include convolutional neural networks and recurrent neural networks.＜/p＞ ＜p＞This book contains all the applied math and programming you need to master the content. Examples range from simple to relatively complex when necessary to ensure acquisition of appropriate deep learning concepts and constructs. Examples are carefully explained, concise, accurate, and complete to perfectly complement deep learning skill development. Care is taken to walk you through the foundational principles of deep learning through clear examples written in Python that you can try out and experiment with using Google Colab from the comfort of your own home or office.＜/p＞ ＜p＞＜strong＞What You Will Learn＜/strong＞＜/p＞ ＜ul＞ ＜li＞Be familiar with the basic concepts and constructs of applied deep learning＜/li＞ ＜li＞Create machine learning models with clean and reliable Python code＜/li＞ ＜li＞Work with datasets common to deep learning applications＜/li＞ ＜li＞Prepare data for TensorFlow consumption＜/li＞ ＜li＞Take advantage of Google Colab’s built-in support for deep learning＜/li＞ ＜li＞Execute deep learning experiments using a variety of neural network models＜/li＞ ＜li＞Be able to mount Google Colab directly to your Google Drive account＜/li＞ ＜li＞Visualize training versus test performance to see model fit＜/li＞ ＜/ul＞ ＜p＞＜strong＞Who This Book Is For＜/strong＞＜/p＞ ＜p＞Readers who want to learn the highly popular TensorFlow 2.x deep learning platform, those who wish to master deep learning fundamentals that are sometimes skipped over in the rush to be productive, and those looking to build competency with a modern cloud service tool such as Google Colab＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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9位
¥3,290 円
評価: 0
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楽天Kobo電子書籍ストア
＜p＞＜strong＞One-stop solution for NLP practitioners, ML developers, and data scientists to build effective NLP systems that can perform real-world complicated tasks＜/strong＞＜/p＞ ＜h4＞Key Features＜/h4＞ ＜ul＞ ＜li＞Apply deep learning algorithms and techniques such as BiLSTMS, CRFs, BPE and more using TensorFlow 2＜/li＞ ＜li＞Explore applications like text generation, summarization, weakly supervised labelling and more＜/li＞ ＜li＞Read cutting edge material with seminal papers provided in the GitHub repository with full working code＜/li＞ ＜/ul＞ ＜h4＞Book Description＜/h4＞ ＜p＞Recently, there have been tremendous advances in NLP, and we are now moving from research labs into practical applications. This book comes with a perfect blend of both the theoretical and practical aspects of trending and complex NLP techniques.＜/p＞ ＜p＞The book is focused on innovative applications in the field of NLP, language generation, and dialogue systems. It helps you apply the concepts of pre-processing text using techniques such as tokenization, parts of speech tagging, and lemmatization using popular libraries such as Stanford NLP and SpaCy. You will build Named Entity Recognition (NER) from scratch using Conditional Random Fields and Viterbi Decoding on top of RNNs.＜/p＞ ＜p＞The book covers key emerging areas such as generating text for use in sentence completion and text summarization, bridging images and text by generating captions for images, and managing dialogue aspects of chatbots. You will learn how to apply transfer learning and fine-tuning using TensorFlow 2.＜/p＞ ＜p＞Further, it covers practical techniques that can simplify the labelling of textual data. The book also has a working code that is adaptable to your use cases for each tech piece.＜/p＞ ＜p＞By the end of the book, you will have an advanced knowledge of the tools, techniques and deep learning architecture used to solve complex NLP problems.＜/p＞ ＜h4＞What you will learn＜/h4＞ ＜ul＞ ＜li＞Grasp important pre-steps in building NLP applications like POS tagging＜/li＞ ＜li＞Use transfer and weakly supervised learning using libraries like Snorkel＜/li＞ ＜li＞Do sentiment analysis using BERT＜/li＞ ＜li＞Apply encoder-decoder NN architectures and beam search for summarizing texts＜/li＞ ＜li＞Use Transformer models with attention to bring images and text together＜/li＞ ＜li＞Build apps that generate captions and answer questions about images using custom Transformers＜/li＞ ＜li＞Use advanced TensorFlow techniques like learning rate annealing, custom layers, and custom loss functions to build the latest DeepNLP models＜/li＞ ＜/ul＞ ＜h4＞Who this book is for＜/h4＞ ＜p＞This is not an introductory book and assumes the reader is familiar with basics of NLP and has fundamental Python skills, as well as basic knowledge of machine learning and undergraduate-level calculus and linear algebra.＜/p＞ ＜p＞The readers who can benefit the most from this book include intermediate ML developers who are familiar with the basics of supervised learning and deep learning techniques and professionals who already use TensorFlow/Python for purposes such as data science, ML, research, analysis, etc.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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10位
¥3,290 円
評価: 0
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楽天Kobo電子書籍ストア
＜p＞＜strong＞Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices＜/strong＞＜/p＞ ＜h4＞Key Features＜/h4＞ ＜ul＞ ＜li＞Introduces and then uses TensorFlow 2 and Keras right from the start＜/li＞ ＜li＞Teaches key machine and deep learning techniques＜/li＞ ＜li＞Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples＜/li＞ ＜/ul＞ ＜h4＞Book Description＜/h4＞ ＜p＞Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.＜/p＞ ＜p＞TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before.＜/p＞ ＜p＞This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.＜/p＞ ＜h4＞What you will learn＜/h4＞ ＜ul＞ ＜li＞Build machine learning and deep learning systems with TensorFlow 2 and the Keras API＜/li＞ ＜li＞Use Regression analysis, the most popular approach to machine learning＜/li＞ ＜li＞Understand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiers＜/li＞ ＜li＞Use GANs (generative adversarial networks) to create new data that fits with existing patterns＜/li＞ ＜li＞Discover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret another＜/li＞ ＜li＞Apply deep learning to natural human language and interpret natural language texts to produce an appropriate response＜/li＞ ＜li＞Train your models on the cloud and put TF to work in real environments＜/li＞ ＜li＞Explore how Google tools can automate simple ML workflows without the need for complex modeling＜/li＞ ＜/ul＞ ＜h4＞Who this book is for＜/h4＞ ＜p＞This book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Some knowledge of machine learning is expected.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。
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