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    Applied Deep Learning with Python. Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions Alex Galea, Luis Capelo

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Applied Deep Learning with Python. Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions Alex Galea, Luis Capelo - okladka książki

    Applied Deep Learning with Python. Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions Alex Galea, Luis Capelo - okladka książki

    Applied Deep Learning with Python. Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions Alex Galea, Luis Capelo - audiobook MP3

    Applied Deep Learning with Python. Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions Alex Galea, Luis Capelo - audiobook CD

    Autorzy:
    Alex Galea, Luis Capelo
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    334
    Dostępne formaty:
         PDF
         ePub
         Mobi
    Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before you train your first predictive model. You’ll then explore a variety of approaches to classification such as support vector networks, random decision forests and k-nearest neighbors to build on your knowledge before moving on to advanced topics.

    After covering classification, you’ll go on to discover ethical web scraping and interactive visualizations, which will help you professionally gather and present your analysis. Next, you’ll start building your keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. You’ll then be guided through a trained neural network, which will help you explore common deep learning network architectures (convolutional, recurrent, and generative adversarial networks) and deep reinforcement learning. Later, you’ll delve into model optimization and evaluation. You’ll do all this while working on a production-ready web application that combines TensorFlow and Keras to produce meaningful user-friendly results.

    By the end of this book, you’ll be equipped with the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively.

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    O autorach książki

    Alex Galea has been professionally practicing data analytics since graduating with a masters degree in physics from the University of Guelph, Canada. He developed a keen interest in Python while researching quantum gases as part of his graduate studies. Alex is currently doing web data analytics, where Python continues to play a key role in his work. He is a frequent blogger about data-centric projects that involve Python and Jupyter Notebooks.
    Luis Capelo is a Harvard-trained analyst and a programmer, who specializes in designing and developing data science products. He is based in New York City, America. Luis is the head of the Data Products team at Forbes, where they investigate new techniques for optimizing article performance and create clever bots that help them distribute their content. He worked for the United Nations as part of the Humanitarian Data Exchange team (founders of the Center for Humanitarian Data). Later on, he led a team of scientists at the Flowminder Foundation, developing models for assisting the humanitarian community. Luis is a native of Havana, Cuba, and the founder and owner of a small consultancy firm dedicated to supporting the nascent Cuban private sector.

    Alex Galea, Luis Capelo - pozostałe książki

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