Download or read book Deep Learning for Search written by Tommaso Teofili and published by Simon and Schuster. This book was released on 2019-06-02 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning for Search teaches you how to improve the effectiveness of your search by implementing neural network-based techniques. By the time you're finished with the book, you'll be ready to build amazing search engines that deliver the results your users need and that get better as time goes on! Foreword by Chris Mattmann. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning handles the toughest search challenges, including imprecise search terms, badly indexed data, and retrieving images with minimal metadata. And with modern tools like DL4J and TensorFlow, you can apply powerful DL techniques without a deep background in data science or natural language processing (NLP). This book will show you how. About the Book Deep Learning for Search teaches you to improve your search results with neural networks. You'll review how DL relates to search basics like indexing and ranking. Then, you'll walk through in-depth examples to upgrade your search with DL techniques using Apache Lucene and Deeplearning4j. As the book progresses, you'll explore advanced topics like searching through images, translating user queries, and designing search engines that improve as they learn! What's inside Accurate and relevant rankings Searching across languages Content-based image search Search with recommendations About the Reader For developers comfortable with Java or a similar language and search basics. No experience with deep learning or NLP needed. About the Author Tommaso Teofili is a software engineer with a passion for open source and machine learning. As a member of the Apache Software Foundation, he contributes to a number of open source projects, ranging from topics like information retrieval (such as Lucene and Solr) to natural language processing and machine translation (including OpenNLP, Joshua, and UIMA). He currently works at Adobe, developing search and indexing infrastructure components, and researching the areas of natural language processing, information retrieval, and deep learning. He has presented search and machine learning talks at conferences including BerlinBuzzwords, International Conference on Computational Science, ApacheCon, EclipseCon, and others. You can find him on Twitter at @tteofili. Table of Contents PART 1 - SEARCH MEETS DEEP LEARNING Neural search Generating synonyms PART 2 - THROWING NEURAL NETS AT A SEARCH ENGINE From plain retrieval to text generation More-sensitive query suggestions Ranking search results with word embeddings Document embeddings for rankings and recommendations PART 3 - ONE STEP BEYOND Searching across languages Content-based image search A peek at performance

Download or read book In Search of Deeper Learning written by Jal Mehta and published by Harvard University Press. This book was released on 2019-04-22 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The best book on high school dynamics I have ever read."--Jay Mathews, Washington Post An award-winning professor and an accomplished educator take us beyond the hype of reform and inside some of America's most innovative classrooms to show what is working--and what isn't--in our schools. What would it take to transform industrial-era schools into modern organizations capable of supporting deep learning for all? Jal Mehta and Sarah Fine's quest to answer this question took them inside some of America's most innovative schools and classrooms--places where educators are rethinking both what and how students should learn. The story they tell is alternately discouraging and hopeful. Drawing on hundreds of hours of observations and interviews at thirty different schools, Mehta and Fine reveal that deeper learning is more often the exception than the rule. And yet they find pockets of powerful learning at almost every school, often in electives and extracurriculars as well as in a few mold-breaking academic courses. These spaces achieve depth, the authors argue, because they emphasize purpose and choice, cultivate community, and draw on powerful traditions of apprenticeship. These outliers suggest that it is difficult but possible for schools and classrooms to achieve the integrations that support deep learning: rigor with joy, precision with play, mastery with identity and creativity. This boldly humanistic book offers a rich account of what education can be. The first panoramic study of American public high schools since the 1980s, In Search of Deeper Learning lays out a new vision for American education--one that will set the agenda for schools of the future.

Download or read book Deep Search written by Konrad Becker and published by . This book was released on 2009 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Search collects 13 texts which investigate the social and political dimensions of how we navigate the deep seas of knowledge. What do we win, and what do we lose when we move from an analogue to a digital information order? How is computer readable significance produced, how is meaning involved in machine communication? Where is the potential of having access to such vast amounts of information? What are the dangers of our reliance on search engines and are there any approaches that do not follow the currently dominating paradigm of Google? This volume answers these questions of culture, context and classification regarding information systems that should not be ignored.

Download or read book In Search of Deep Faith written by Jim Belcher and published by InterVarsity Press. This book was released on 2013-11-19 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: Follow pastor Jim Belcher and his family as they take a pilgrimage through Europe, seeking substance for their faith in Christianity's historic, civilizational home. What they find, in places like Lewis's Oxford and Bonhoeffer's Germany, are glimpses of another kind of faith—one with power to cut through centuries and pierce our hearts today.

Download or read book Evolutionary Deep Neural Architecture Search Fundamentals Methods and Recent Advances written by Yanan Sun and published by Springer Nature. This book was released on 2022-11-08 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically narrates the fundamentals, methods, and recent advances of evolutionary deep neural architecture search chapter by chapter. This will provide the target readers with sufficient details learning from scratch. In particular, the method parts are devoted to the architecture search of unsupervised and supervised deep neural networks. The people, who would like to use deep neural networks but have no/limited expertise in manually designing the optimal deep architectures, will be the main audience. This may include the researchers who focus on developing novel evolutionary deep architecture search methods for general tasks, the students who would like to study the knowledge related to evolutionary deep neural architecture search and perform related research in the future, and the practitioners from the fields of computer vision, natural language processing, and others where the deep neural networks have been successfully and largely used in their respective fields.

Download or read book Practical Deep Learning for Cloud Mobile and Edge written by Anirudh Koul and published by "O'Reilly Media, Inc.". This book was released on 2019-10-14 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users

Download or read book Gone Too Deep written by Katie Ruggle and published by Sourcebooks, Inc.. This book was released on 2016-08-02 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Vivid and charming."—CHARLAINE HARRIS, #1 New York Times bestselling author of the Sookie Stackhouse series George is a mystery. Tall. Dark. Intense. And she'll need him by her side if she wants to survive. George Holloway has spent his life alone, exploring the treacherous beauty of the Colorado Rockies. He's the best survival expert Search & Rescue has, which makes him the obvious choice to lead Ellie Price through deadly terrain to find her missing father. There's just one problem—Ellie's everything George isn't. She's a city girl, charming, gregarious, delicate, small. And when she looks up at him with those big, dark eyes, he swears he would tear the world apart to keep her safe. Ellie's determined to find her father no matter the cost. But as she and her gorgeous mountain of a guide fight their way through an unforgiving wilderness, they find themselves in the crosshairs of a dangerous man in search of revenge. And they are now his prey... In the remote Rocky Mountains, lives depend on the Search & Rescue brotherhood. But in a place this far off the map, trust is hard to come by and secrets can be murder... "Gripping suspense, unique heroines, sexy heroes." —CHRISTINE FEEHAN, #1 New York Times Bestselling Author Search and Rescue Series: On His Watch (FREE novella) Hold Your Breath (Book 1) Fan the Flames (Book 2) Gone too Deep (Book 3) In Safe Hands (Book 4) After the End (FREE novella) What People Are Saying About Katie Ruggle's Romantic Suspense: "I love Ruggle's characters. They're sharply drawn, and vividly alive. I'm happy when they find each other. These are wonderful escapist books."—CHARLAINE HARRIS, #1 New York Times Bestselling author of the Sookie Stackhouse series "Sexy and suspenseful, I couldn't turn the pages fast enough."—JULIE ANN WALKER, New York Times and USA Today Bestselling Author for Hold Your Breath "Chills and thrills and a sexy slow-burning romance from a terrific new voice."—D.D. AYRES, author of the K-9 Rescue Series for Hold Your Breath

Download or read book Deep Dark Forest written by Susan Lund and published by Susan Lund. This book was released on 2023-02-27 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introducing DEEP DARK FOREST: the second book in The Dark Series: A Michael Carter Cold Case Thriller by bestselling crime thriller author Susan Lund. Too many secrets are hidden in the deep dark forest… When the skeletal remains of a missing college student are found deep in the forest on the property of wealthy and prominent family, King County Cold Case Investigator Michael Carter and King County Medical Examiner Dr. Grace Keller are on the case. Wealthy, powerful and influential, the family has been above suspicion – until now… With leading legal professionals and powerful law enforcement members, the family has been a pillar of the community for decades, and appears to be above reproach, but Michael can’t help but dig deeper. He and Dr. Keller review the deaths connected to the family, including several that have been ruled accidental or natural. The killer knows every trick in the book... The family has friends in high places who work to protect them when Michael Carter’s investigation gets too close for comfort.

Download or read book Deep Time written by Henry Gee and published by HarperCollins UK. This book was released on 2008-06 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work introduces a revolution in how we look at the history of life, and humanity's place within it. Cladistics overturns the traditional linear theories of evolution and shows the possibility of creatures far wilder than human imagination.

Download or read book In Search Of Deep Throat written by Leonard Garment and published by Basic Books. This book was released on 2001-04-17 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: More than a quarter century after Bob Woodward introduced his Scotch-drinking, cigarette-smoking, garage-skulking friend and source in All the President's Men, the public remains enduringly engrossed by the mystery of Deep Throat's identity. Leonard Garment became fascinated himself and began his own search for Deep Throat. This is the story of that hunt and its successful outcome, a hunt conducted in quintessential Washington fashion: at lunches, dinners, and parties, through the examination of secret, classified documents and testimony, and assisted by liberal doses of political gossip and insider tips from Woodward himself.

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Download or read book In Search of Lost Futures written by Magdalena Kazubowski-Houston and published by Springer Nature. This book was released on 2021-02-16 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Search of Lost Futures asks how imaginations might be activated through practices of autoethnography, multimodality, and deep interdisciplinarity—each of which has the power to break down methodological silos, cultivate novel research sensibilities, and inspire researchers to question what is known about ethnographic process, representation, reflexivity, audience, and intervention within and beyond the academy. By blurring the boundaries between the past, present, and future; between absence and presence; between the possible and the impossible; and between fantasy and reality, In Search of Lost Futures pushes the boundaries of ethnographic engagement. It reveals how researchers on the cutting edge of the discipline are studying absence and grief and employing street performance, museum exhibit, anticipation, or simulated reality to research and intervene in the possible, the impossible, and the uncertain.

Download or read book In Search of Soul written by Alejandro Nava and published by Univ of California Press. This book was released on 2017-09-12 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Search of Soul explores the meaning of “soul” in sacred and profane incarnations, from its biblical origins to its central place in the rich traditions of black and Latin history. Surveying the work of writers, artists, poets, musicians, philosophers and theologians, Alejandro Nava shows how their understandings of the “soul” revolve around narratives of justice, liberation, and spiritual redemption. He contends that biblical traditions and hip-hop emerged out of experiences of dispossession and oppression. Whether born in the ghettos of America or of the Roman Empire, hip-hop and Christianity have endured by giving voice to the persecuted. This book offers a view of soul in living color, as a breathing, suffering, dreaming thing.

Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Download or read book Machine Learning with TensorFlow Second Edition written by Mattmann A. Chris and published by Manning Publications. This book was released on 2021-02-02 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Summary Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Supercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. You don’t have to be a mathematician to use ML: Tools like Google’s TensorFlow library help with complex calculations so you can focus on getting the answers you need. About the book Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10. What's inside Machine Learning with TensorFlow Choosing the best ML approaches Visualizing algorithms with TensorBoard Sharing results with collaborators Running models in Docker About the reader Requires intermediate Python skills and knowledge of general algebraic concepts like vectors and matrices. Examples use the super-stable 1.15.x branch of TensorFlow and TensorFlow 2.x. About the author Chris Mattmann is the Division Manager of the Artificial Intelligence, Analytics, and Innovation Organization at NASA Jet Propulsion Lab. The first edition of this book was written by Nishant Shukla with Kenneth Fricklas. Table of Contents PART 1 - YOUR MACHINE-LEARNING RIG 1 A machine-learning odyssey 2 TensorFlow essentials PART 2 - CORE LEARNING ALGORITHMS 3 Linear regression and beyond 4 Using regression for call-center volume prediction 5 A gentle introduction to classification 6 Sentiment classification: Large movie-review dataset 7 Automatically clustering data 8 Inferring user activity from Android accelerometer data 9 Hidden Markov models 10 Part-of-speech tagging and word-sense disambiguation PART 3 - THE NEURAL NETWORK PARADIGM 11 A peek into autoencoders 12 Applying autoencoders: The CIFAR-10 image dataset 13 Reinforcement learning 14 Convolutional neural networks 15 Building a real-world CNN: VGG-Face ad VGG-Face Lite 16 Recurrent neural networks 17 LSTMs and automatic speech recognition 18 Sequence-to-sequence models for chatbots 19 Utility landscape

Download or read book Data Analytics and AI written by Jay Liebowitz and published by CRC Press. This book was released on 2020-08-06 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data.

Download or read book Casting Light on the Dark Web written by Matthew Beckstrom and published by Rowman & Littlefield. This book was released on 2019-09-05 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covers topics from what the dark web is, to how it works, to how you can use it, to some of the myths surrounding it. Casting Light on the Dark Web: A Guide for Safe Exploration is an easy-to-read and comprehensive guide to understanding how the Dark Web works and why you should be using it! Readers will be led on a tour of this elusive technology from how to download the platform for personal or public use, to how it can best be utilized for finding information. This guide busts myths and informs readers, while remaining jargon-free and entertaining. Useful for people of all levels of internet knowledge and experience.