Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training 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, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide
The history of ideas provides an important means of understanding and reinterpreting the literature of the past; and in this study Dr. Calder demonstrates the illumination that this informed approach brings to the comedies of MoliFre. In the course of this study, the author outlines a fresh theory of classical comedy which applies to the works of other French writers of the 17th century; and the historical reinterpretations of MoliFre's two most difficult plays -- Le Tartuffe and Dom Juan -- break entirely new ground.Although this is a work which specialists will admire, it is also intended to serve as an introduction to MoliFre and French classical comedy at large and will be of considerable value to younger students and readers of MoliFre in general.
Can a speaker's words ever be faithfully reported? History, philosophy, ethnography, political theory, linguistics, and literary criticism all involve debates about discourse and representation. By drawing from Plato's theory of discourse, the lively analysis of speech presentation in this book provides a coherent and original contribution to these debates, and highlights the problems involved when speech becomes both the object and the medium of narrative representation. The opening chapters offer fresh insights on ideology, intertextuality, literary language, and historiography, and reveal important connections between them. These insights are then applied in specific critical treatments of - Virgil's Aeneid, of Petronius' Satyricon, and of scenes involving messengers and angels in classical and European epic. Throughout this study, ancient texts are discussed in conjunction with examples from later traditions. Overall, this book uses Latin literature to demonstrate the theoretical and ideological importance of speech presentation for a number of contemporary disciplines.
This work presents in detail a description of archaeological data from the Iron II temple complex at Tel Dan in northern Israel. Davis analyzes the archaeological remains from the ninth and eighth centuries, paying close attention to how the temple functioned as sacred space. Correlating the archaeological data with biblical depictions of worship, especially the “textual strata” of 1 Kings 18 and the book of Amos, Davis argues that the temple was the site of “official” and family religion and that worship at the temple became increasingly centralized. Tel Dan's role in helping reconstruct ancient Israelite religion, especially distinctive religious traditions of the northern kingdom, is also considered.
A reconstruction of the life and works of a sixteenth-century minstrel, showing the tradition to be flourishing well into the Tudor period. Richard Sheale, a harper and balladeer from Tamworth, is virtually the only English minstrel whose life story is known to us in any detail. It had been thought that by the sixteenth century minstrels had generally been downgradedto the role of mere jesters. However, through a careful examination of the manuscript which Sheale almost certainly "wrote" (Bodleian Ashmole 48) and other records, the author argues that the oral tradition remained vibrant at this period, contrary to the common idea that print had by this stage destroyed traditional minstrelsy. The author shows that under the patronage of Edward Stanley, earl of Derby, and his son, from one of the most important aristocratic families in England, Sheale recited and collected ballads and travelled to and from London to market them. Amongst his repertoire was the famous Chevy Chase, which Sir Philip Sidney said moved his heart "more than witha trumpet". Sheale also composed his own verse, including a lament on being robbed of 60 on his way to London; the poem is reproduced in this volume. ANDREW TAYLOR lectures in the Department of English, University of Ottawa.
Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training 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, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide
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