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    The Deep Learning with Keras Workshop. Learn how to define and train neural network models with just a few lines of code

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    Matthew Moocarme, Mahla Abdolahnejad, Ritesh Bhagwat
    The Deep Learning with Keras Workshop. Learn how to define and train neural network models with just a few lines of code Matthew Moocarme, Mahla Abdolahnejad, Ritesh Bhagwat - okladka książki

    The Deep Learning with Keras Workshop. Learn how to define and train neural network models with just a few lines of code Matthew Moocarme, Mahla Abdolahnejad, Ritesh Bhagwat - okladka książki

    The Deep Learning with Keras Workshop. Learn how to define and train neural network models with just a few lines of code Matthew Moocarme, Mahla Abdolahnejad, Ritesh Bhagwat - audiobook MP3

    The Deep Learning with Keras Workshop. Learn how to define and train neural network models with just a few lines of code Matthew Moocarme, Mahla Abdolahnejad, Ritesh Bhagwat - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    496
    Dostępne formaty:
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         Mobi

    Ebook (29,90 zł najniższa cena z 30 dni)

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    Do przechowalni

    New experiences can be intimidating, but not this one! This beginner’s guide to deep learning is here to help you explore deep learning from scratch with Keras, and be on your way to training your first ever neural networks.

    What sets Keras apart from other deep learning frameworks is its simplicity. With over two hundred thousand users, Keras has a stronger adoption in industry and the research community than any other deep learning framework.

    The Deep Learning with Keras Workshop starts by introducing you to the fundamental concepts of machine learning using the scikit-learn package. After learning how to perform the linear transformations that are necessary for building neural networks, you'll build your first neural network with the Keras library. As you advance, you'll learn how to build multi-layer neural networks and recognize when your model is underfitting or overfitting to the training data. With the help of practical exercises, you’ll learn to use cross-validation techniques to evaluate your models and then choose the optimal hyperparameters to fine-tune their performance. Finally, you’ll explore recurrent neural networks and learn how to train them to predict values in sequential data.

    By the end of this book, you'll have developed the skills you need to confidently train your own neural network models.

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    O autorach książki

    Matthew Moocarme is an accomplished data scientist with more than eight years of experience in creating and utilizing machine learning models. He comes from a background in the physical sciences, in which he holds a Ph.D. in physics from the Graduate Center of CUNY. Currently, he leads a team of data scientists and engineers in the media and advertising space to build and integrate machine learning models for a variety of applications. In his spare time, Matthew enjoys sharing his knowledge with the data science community through published works, conference presentations, and workshops.
    Mahla Abdolahnejad is a Ph.D. candidate in systems and computer engineering with Carleton University, Canada. She also holds a bachelor's degree and a master's degree in biomedical engineering, which first exposed her to the field of artificial intelligence and artificial neural networks, in particular. Her Ph.D. research is focused on deep unsupervised learning for computer vision applications. She is particularly interested in exploring the differences between a human's way of learning from the visual world and a machine's way of learning from the visual world, and how to push machine learning algorithms toward learning and thinking like humans.
    Ritesh Bhagwat has a master's degree in applied mathematics with a specialization in computer science. He has over 14 years of experience in data-driven technologies and has led and been a part of complex projects ranging from data warehousing and business intelligence to machine learning and artificial intelligence. He has worked with top-tier global consulting firms as well as large multinational financial institutions. Currently, he works as a data scientist. Besides work, he enjoys playing and watching cricket and loves to travel. He is also deeply interested in Bayesian statistics.

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