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    Deep Learning with R for Beginners. Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado

    (ebook) (audiobook) (audiobook) Książka w języku angielskim
    Deep Learning with R for Beginners. Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado - okladka książki

    Deep Learning with R for Beginners. Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado - okladka książki

    Deep Learning with R for Beginners. Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado - audiobook MP3

    Deep Learning with R for Beginners. Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado - audiobook CD

    Autorzy:
    Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Dostępne formaty:
         PDF
         ePub
         Mobi
    Deep learning has a range of practical applications in several domains, while R is the preferred language for designing and deploying deep learning models.

    This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The Learning Path will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R.

    By the end of this Learning Path, you’ll be well-versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects.

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    O autorach książki

    Mark Hodnett is a data scientist with over 20 years of industry experience in software development, business intelligence systems, and data science. He has worked in a variety of industries, including CRM systems, retail loyalty, IoT systems, and accountancy. He holds a master's in data science and an MBA. He works in Cork, Ireland, as a senior data scientist with AltViz.
    Joshua F. Wiley is a lecturer at Monash University, conducting quantitative research on sleep, stress, and health. He earned his Ph.D. from the University of California, Los Angeles and completed postdoctoral training in primary care and prevention. In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. He develops or co-develops a number of R packages including Varian, a package to conduct Bayesian scale-location structural equation models, and MplusAutomation, a popular package that links R to the commercial Mplus software.

    Yuxi (Hayden) Liu rozwija modele uczenia maszynowego w Google. Wcześniej pracował naukowo nad zastosowaniami uczenia maszynowego w takich dziedzinach jak reklama internetowa i cyberbezpieczeństwo. Jest entuzjastą edukacji i autorem wielu książek o uczeniu maszynowym. Pierwsze wydanie tego podręcznika zajmowało wiodącą pozycję w rankingu Amazona w latach 2017 i 2018.

    Pablo Maldonado is an applied mathematician and data scientist who has had a taste for software development since his days of programming BASIC on a Tandy 1000. As an academic and business consultant, he spends a great deal of his time building applied artificial intelligence solutions for text analytics, sensor and transactional data, and reinforcement learning. Pablo earned his Ph.D. in applied mathematics (with a focus on mathematical game theory) at the Universite Pierre et Marie Curie in Paris, France.

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