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    Hands-On Computer Vision with TensorFlow 2. Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras Benjamin Planche, Eliot Andres

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Hands-On Computer Vision with TensorFlow 2. Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras Benjamin Planche, Eliot Andres - okladka książki

    Hands-On Computer Vision with TensorFlow 2. Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras Benjamin Planche, Eliot Andres - okladka książki

    Hands-On Computer Vision with TensorFlow 2. Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras Benjamin Planche, Eliot Andres - audiobook MP3

    Hands-On Computer Vision with TensorFlow 2. Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras Benjamin Planche, Eliot Andres - audiobook CD

    Autorzy:
    Benjamin Planche, Eliot Andres
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    372
    Dostępne formaty:
         PDF
         ePub
         Mobi
    Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. This book will help you explore TensorFlow 2, the brand new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks.
    Hands-On Computer Vision with TensorFlow 2 starts with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the book demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts.
    By the end of the book, you will have both the theoretical understanding and practical skills to solve advanced computer vision problems with TensorFlow 2.0.

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    O autorach książki

    Dr. Benjamin Planche is a passionate research scientist in computer vision and machine learning. His main research efforts focus on data scarcity problems and industrial vision systems, leading to numerous patents and publications at international conferences. He worked in various research labs around the world (including in France, Japan, Germany, and the USA).

    Benjamin obtained his Ph.D. summa cum laude from the Faculty of Computer Science and Mathematics at the University of Passau, under the supervision of Prof. Dr. Harald Kosch. He also has a double master's degree from INSA-Lyon (France) and the University of Passau (Germany), with first-class honors and a multinational excellence award. He also likes sharing his knowledge and experience on various platforms or applying them to the creation of aesthetic demos.
    Eliot Andres is a freelance deep learning and computer vision engineer. He has more than 3 years' experience in the field, applying his skills to a variety of industries, such as banking, health, social media, and video streaming. Eliot has a double master's degree from cole des Ponts and Tlcom, Paris.

    His focus is industrialization: delivering value by applying new technologies to business problems. Eliot keeps his knowledge up to date by publishing articles on his blog and by building prototypes using the latest technologies.

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