deep learning a practitioners approach

Download Book Deep Learning A Practitioners Approach in PDF format. You can Read Online Deep Learning A Practitioners Approach here in PDF, EPUB, Mobi or Docx formats.

Deep Learning

Author : Adam Gibson
ISBN : 1491914254
Genre : Computers
File Size : 53. 10 MB
Format : PDF, ePub, Mobi
Download : 323
Read : 979

Download Now


Looking for one central source where you can learn key findings on machine learning? Deep Learning: The Definitive Guide provides developers and data scientists with the most practical information available on the subject, including deep learning theory, best practices, and use cases. Authors Adam Gibson and Josh Patterson present the latest relevant papers and techniques in a non­academic manner, and implement the core mathematics in their DL4J library. If you work in the embedded, desktop, and big data/Hadoop spaces and really want to understand deep learning, this is your book.

Fundamentals Of Deep Learning

Author : Nikhil Buduma
ISBN : 1491925612
Genre :
File Size : 72. 74 MB
Format : PDF, ePub, Docs
Download : 959
Read : 1298

Download Now


With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research that is paving the way for modern machine learning. This book uses exposition and examples to help you understand major concepts in this complicated field. Large companies such as Google, Microsoft, and Facebook have taken notice, and are actively growing in-house deep learning teams. For the rest of us however, deep learning is still a pretty complex and difficult subject to grasp. If you have a basic understanding of what machine learning is, have familiarity with the Python programming language, and have some mathematical background with calculus, this book will help you get started.

Deep Learning

Author : Li Deng
ISBN : 1601988141
Genre : Computers
File Size : 53. 74 MB
Format : PDF, ePub, Mobi
Download : 435
Read : 927

Download Now


Deep learning, Machine learning, Artificial intelligence, Neural networks, Deep neural networks, Deep stacking networks, Autoencoders, Supervised learning, Unsupervised learning, Hybrid deep networks, Object recognition, Computer vision, Natural language processing, Language models, Multi-task learning, Multi-modal processing

Deep Learning

Author : Stellan Ohlsson
ISBN : 9781139496759
Genre : Psychology
File Size : 50. 30 MB
Format : PDF
Download : 474
Read : 361

Download Now


Although the ability to retain, process, and project prior experience onto future situations is indispensable, the human mind also possesses the ability to override experience and adapt to changing circumstances. Cognitive scientist Stellan Ohlsson analyzes three types of deep, non-monotonic cognitive change: creative insight, adaptation of cognitive skills by learning from errors, and conversion from one belief to another, incompatible belief. For each topic, Ohlsson summarizes past research, re-formulates the relevant research questions, and proposes information-processing mechanisms that answer those questions. The three theories are based on the principles of redistribution of activation, specialization of practical knowledge, and re-subsumption of declarative information. Ohlsson develops the implications of those mechanisms by scaling their effects with respect to time, complexity, and social interaction. The book ends with a unified theory of non-monotonic cognitive change that captures the abstract properties that the three types of change share.

Machine Learning

Author : Kevin P. Murphy
ISBN : 9780262018029
Genre : Computers
File Size : 71. 70 MB
Format : PDF, ePub, Docs
Download : 958
Read : 469

Download Now


A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Automatic Speech Recognition

Author : Dong Yu
ISBN : 9781447157793
Genre : Technology & Engineering
File Size : 30. 17 MB
Format : PDF, ePub
Download : 677
Read : 1300

Download Now


This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.

Applied Predictive Modeling

Author : Max Kuhn
ISBN : 9781461468493
Genre : Medical
File Size : 26. 24 MB
Format : PDF, Kindle
Download : 123
Read : 1041

Download Now


This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance—all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code for each step of the process. The data sets and corresponding code are available in the book’s companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. Readers and students interested in implementing the methods should have some basic knowledge of R. And a handful of the more advanced topics require some mathematical knowledge.

Top Download:

Best Books