deep learning a practitioners approach

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Deep Learning

Author : Adam Gibson
ISBN : 1491914254
Genre : Computers
File Size : 83. 96 MB
Format : PDF, Mobi
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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 nonĀ­academic 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.

Deep Learning

Author : Ian Goodfellow
ISBN : 9780262035613
Genre : Computers
File Size : 76. 64 MB
Format : PDF, ePub, Mobi
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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.

Fundamentals Of Deep Learning

Author : Nikhil Buduma
ISBN : 1491925612
Genre : Computers
File Size : 84. 2 MB
Format : PDF, Kindle
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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 : 27. 34 MB
Format : PDF
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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

Machine Learning

Author : Kevin P. Murphy
ISBN : 9780262018029
Genre : Computers
File Size : 49. 28 MB
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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 : 47. 11 MB
Format : PDF
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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.

Practical Machine Learning With H2o

Author : Darren Cook
ISBN : 149196460X
Genre : Computers
File Size : 84. 7 MB
Format : PDF, Mobi
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Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that's easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms. If you're familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets. You'll explore several modern machine-learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning. Learn how to import, manipulate, and export data with H2O Explore key machine-learning concepts, such as cross-validation and validation data sets Work with three diverse data sets, including a regression, a multinomial classification, and a binomial classification Use H2O to analyze each sample data set with four supervised machine-learning algorithms Understand how cluster analysis and other unsupervised machine-learning algorithms work

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