PAC | Federated Learning With TensorFlow (2019 EN)

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  1. Kanka

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    Company: Packt Publishing
    Author: Jakub Konczyk
    Full Title: Federated Learning With TensorFlow
    Year: 2019
    Language: English
    Genre: Educational: Big Data
    Skill Level: -
    Price: €124.99
    Files: MP4 (+ Code Files)
    Time: 02:04:52
    Video: AVC, 1920 x 1080 (1.778) at 30.000 fps, 400 kbps
    Audio: AAC at 144 Kbps, 2 channels, 48.0 KHz

    Train models using distributed data from a variety of mobile devices to classify images, generate text, and do sentiment analysis.

    Federated Learning is revolutionizing how Machine Learning models are trained. TensorFlow Federated is the first production-level federated learning platform that makes it easy to build mobile device learning-based applications. In this course, you’ll learn the basics of building Federated Learning models that can be gradually improved by decentralized data that comes from a variety of mobile devices while not violating the privacy of end users.

    You’ll start by exploring the nature of problems that TensorFlow Federated helps to solve and you’ll install the necessary software. After that, we’ll jump straight into improving an image classification model using a bunch of samples of decentralized data from specially prepared MNIST dataset. Then you’ll start working with text and apply Federated Learning to text generation using a pre-trained model on Charles Dickens' texts. Next, you’ll handle a text classification problem with TensorFlow Federated where you’ll use a movie reviews dataset.

    By the end of this course, you’ll have the practical skills to prepare both datasets and models for Federated Learning as well as the ability to train and evaluate your own models in TensorFlow Federated.

    ✓ Quickly install all the necessary tools to practice Federated Learning on your own machine
    ✓ Apply Federated Learning on a variety of common Deep Learning problems
    ✓ Gradually improve your pre-trained models using decentralized data
    ✓ Discover what to look for when applying the TensorFlow Federated framework in your own projects
    ✓ Train and evaluate your own models in a Federated Learning fashion

    ✓ Get acquainted with Federated Learning, an emerging technology that has the potential to disrupt cloud computing
    ✓ Train Machine Learning models using decentralized data residing on end devices such as mobile phones with Federated Learning
    ✓ Discover how Federated Learning can be applied to the most popular Deep Learning models on decentralized data in your own projects

    1. Introduction to Federated Learning
    01. The Course Overview
    02. The Main Problem That TensorFlow Federated Is Solving
    03. Installing All the Necessary Tools on Your Local Computer
    2. Image Classification
    04. Load Decentralized MNIST Dataset
    05. Set up an Image Classification Model
    06. Train, Test, and Evaluate the Model
    3. Text Generation
    07. Load and Prepare an Example Decentralized Text Dataset
    08. Prepare a Pre-Trained Text Generation Model for FL
    09. Train, Test, and Evaluate the Model
    4. Text Classification
    10. Choose and Prepare the Right Dataset for Decentralized Text Classification
    11. Create and Prepare the Model
    12. Train, Test, and Evaluate a Text Classification Model
    5. Using TensorFlow Federated in Your Own Project
    13. What Is Currently Possible with TensorFlow Federated
    14. Dataset and Model Requirements
    15. Deploying TensorFlow on a Variety of Mobile Devices
    16. Case Study – Federated Learning in Gboard