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    Applied Deep Learning with Keras. Solve complex real-life problems with the simplicity of Keras Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Applied Deep Learning with Keras. Solve complex real-life problems with the simplicity of Keras Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme - okladka książki

    Applied Deep Learning with Keras. Solve complex real-life problems with the simplicity of Keras Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme - okladka książki

    Applied Deep Learning with Keras. Solve complex real-life problems with the simplicity of Keras Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme - audiobook MP3

    Applied Deep Learning with Keras. Solve complex real-life problems with the simplicity of Keras Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme - audiobook CD

    Autorzy:
    Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    412
    Dostępne formaty:
         PDF
         ePub
         Mobi
    Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code.

    Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the book guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model.

    By the end of this book, you will have the skills you need to use Keras when building high-level deep neural networks.

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    O autorach książki

    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.
    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.
    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.

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