Deep learning from the ground up using R and the powerful Keras library! In Deep Learning with R, Second Edition you will learn: Deep learning from first principles Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Deep Learning with R, Second Edition shows you how to put deep learning into action. It’s based on the revised new edition of François Chollet’s bestselling Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R. About the book Deep Learning with R, Second Edition is a hands-on guide to deep learning using the R language. As you move through this book, you’ll quickly lock in the foundational ideas of deep learning. The intuitive explanations, crisp illustrations, and clear examples guide you through core DL skills like image processing and text manipulation, and even advanced features like transformers. This revised and expanded new edition is adapted from Deep Learning with Python, Second Edition by François Chollet, the creator of the Keras library. What's inside Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation About the reader For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required. About the author François Chollet is a software engineer at Google and creator of Keras. Tomasz Kalinowski is a software engineer at RStudio and maintainer of the Keras and Tensorflow R packages. J.J. Allaire is the founder of RStudio, and the author of the first edition of this book. Table of Contents 1 What is deep learning? 2 The mathematical building blocks of neural networks 3 Introduction to Keras and TensorFlow 4 Getting started with neural networks: Classification and regression 5 Fundamentals of machine learning 6 The universal workflow of machine learning 7 Working with Keras: A deep dive 8 Introduction to deep learning for computer vision 9 Advanced deep learning for computer vision 10 Deep learning for time series 11 Deep learning for text 12 Generative deep learning 13 Best practices for the real world 14 Conclusions
Starting from quantum mechanical and condensed matter foundations, this book introduces into the necessary theory behind spin electronics (Spintronics). Equations of spin diffusion, -evolution and -tunelling are provided before an overview is given of simulation of spin transport at the atomic scale. Furthermore, applications are discussed with a focus on elementary spintronics devices such as spin valves, memory cells and hard disk heads.
Transcultural things examines four sets of artefacts from the Polish-Lithuanian Commonwealth: maps pointing to Poland–Lithuania’s roots in the supposedly ‘Oriental’ land of Sarmatia, portrayals of fashions that purport to trace Polish culture back to a distant and revered past, Ottomanesque costumes worn by Polish ambassadors and carpets labelled as Polish despite their foreign provenance. These examples of invented tradition borrowed from abroad played a significant role in narrating and visualising the cultural landscape of Polish-Lithuanian elites. But while modern scholarship defines these objects as exemplars of national heritage, early modern beholders treated them with more flexibility, seeing no contradiction in framing material things as local cultural forms while simultaneously acknowledging their foreign derivation. The book reveals how artefacts began to signify as vernacular idioms in the first place, often through obscuring their non-local origin and tainting subsequent discussions of the imagined purity of national culture as a result.
A quantitative study of the pre-war population of Piotrków Trybunalski in Central Poland reveals key demographic similarities and differences between local Jews and non-Jews and places them in a European perspective.
In Images of China in Polish and Serbian Travel Writings (1720-1949), Tomasz Ewertowski examines how Polish and Serbian travelers from the 18th to the mid-20th century described China, showing various factors which influenced their representations of the Middle Kingdom.
The book is the result of the National Science Centre’s project entitled ‘Social engineering. Projects of nation-state building and their representation in historiography and historical memory: Croatia, Germany, Poland and Ukraine in the twentieth century’. The project was conducted at the Institute of Political Studies of the Polish Academy of Sciences (PAN). The aim of the participants in the project, developed jointly by the Department of German Studies and the Department of History of Eastern Territories, was to provide a broad perspective on nation-building processes in Central Europe in the nineteenth and twentieth centuries and to determine the place of projects on population policy (social engineering) in these processes. The authors also analyse the role of the memory of these projects in developing nation states in this region of Europe in the second half of the twentieth century and contemporary times. The subjects analysed cover a broad spectrum of issues related to the emergence of modern states, demography, eugenics, racial hygiene, statistics, geography and specific policies – from supporting the birth of preferred groups to genocide. The book concerns both the development of modern societies and the problems of nationalism, racial ideology and the idea of ‘the body of the nation’.
Deep learning from the ground up using R and the powerful Keras library! In Deep Learning with R, Second Edition you will learn: Deep learning from first principles Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Deep Learning with R, Second Edition shows you how to put deep learning into action. It’s based on the revised new edition of François Chollet’s bestselling Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R. About the book Deep Learning with R, Second Edition is a hands-on guide to deep learning using the R language. As you move through this book, you’ll quickly lock in the foundational ideas of deep learning. The intuitive explanations, crisp illustrations, and clear examples guide you through core DL skills like image processing and text manipulation, and even advanced features like transformers. This revised and expanded new edition is adapted from Deep Learning with Python, Second Edition by François Chollet, the creator of the Keras library. What's inside Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation About the reader For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required. About the author François Chollet is a software engineer at Google and creator of Keras. Tomasz Kalinowski is a software engineer at RStudio and maintainer of the Keras and Tensorflow R packages. J.J. Allaire is the founder of RStudio, and the author of the first edition of this book. Table of Contents 1 What is deep learning? 2 The mathematical building blocks of neural networks 3 Introduction to Keras and TensorFlow 4 Getting started with neural networks: Classification and regression 5 Fundamentals of machine learning 6 The universal workflow of machine learning 7 Working with Keras: A deep dive 8 Introduction to deep learning for computer vision 9 Advanced deep learning for computer vision 10 Deep learning for time series 11 Deep learning for text 12 Generative deep learning 13 Best practices for the real world 14 Conclusions
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