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    Machine Learning with TensorFlow, Second Edition, Video Edition

    Posted By: lucky_aut
    Machine Learning with TensorFlow, Second Edition, Video Edition

    Machine Learning with TensorFlow, Second Edition, Video Edition
    Published: 01/2021
    Duration: 10h 48m | Video: .MP4, 1920x1080 30 fps | Audio: AAC, 44.1kHz, 2ch | Size: 1.63 GB
    Genre: eLearning | Language: English

    In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.

    Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers.

    About the Technology
    Supercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. You don’t have to be a mathematician to use ML: Tools like Google’s TensorFlow library help with complex calculations so you can focus on getting the answers you need.

    About the Book
    Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10.

    What's Inside
    Machine Learning with TensorFlow
    Choosing the best ML approaches
    Visualizing algorithms with TensorBoard
    Sharing results with collaborators
    Running models in Docker