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    Hands-On Deep Learning Architectures with Python. Create deep neural networks to solve computational problems using TensorFlow and Keras

    (ebook) (audiobook) (audiobook) Język publikacji: angielski
    Hands-On Deep Learning Architectures with Python. Create deep neural networks to solve computational problems using TensorFlow and Keras Yuxi (Hayden) Liu, Saransh Mehta - okładka ebooka

    Hands-On Deep Learning Architectures with Python. Create deep neural networks to solve computational problems using TensorFlow and Keras Yuxi (Hayden) Liu, Saransh Mehta - okładka ebooka

    Hands-On Deep Learning Architectures with Python. Create deep neural networks to solve computational problems using TensorFlow and Keras Yuxi (Hayden) Liu, Saransh Mehta - okładka audiobooka MP3

    Hands-On Deep Learning Architectures with Python. Create deep neural networks to solve computational problems using TensorFlow and Keras Yuxi (Hayden) Liu, Saransh Mehta - okładka audiobooks CD

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    316
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    Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems.
    Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations.
    By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world.

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    O autorach ebooka

    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.

    Saransh Mehta has cross-domain experience of working with texts, images, and audio using deep learning. He has been building artificial, intelligence-based solutions, including a generative chatbot, an attendee-matching recommendation system, and audio keyword recognition systems for multiple start-ups. He is very familiar with the Python language, and has extensive knowledge of deep learning libraries such as TensorFlow and Keras. He has been in the top 10% of entrants to deep learning challenges hosted by Microsoft and Kaggle.

    Yuxi (Hayden) Liu, Saransh Mehta - pozostałe książki

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