Building Convolutional Neural Networks on Google Cloud

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    Building Convolutional Neural Networks on Google Cloud

    Building Convolutional Neural Networks on Google Cloud

    Once you know how to build and train neural networks using TensorFlow and Google Cloud Machine Learning Engine, what’s next? Before long, you’ll discover that prebuilt estimators and default configurations will only get you so far.

    To optimize your models, you may need to create your own estimators, try different techniques to reduce overfitting, and use custom clusters to train your models.

    Building Convolutional Neural Networks on Google Cloud

    Convolutional Neural Networks (CNNs) are very good at certain tasks, especially recognizing objects in pictures and videos. In fact, they’re one of the technologies powering self-driving cars. In this course, you’ll follow hands-on examples to build a CNN, train it using a custom scale tier on Machine Learning Engine, and visualize its performance. You’ll also learn how to recognize overfitting and apply different methods to avoid it.

    Learning Objectives

    Build a Convolutional Neural Network in TensorFlow
    Analyze a model’s training performance using TensorBoard
    Identify cases of overfitting and apply techniques to prevent it
    Scale a Cloud ML Engine job using a custom configuration
    Intended Audience
    Data professionals
    People studying for the Google Certified Professional Data Engineer exam
    Prerequisites
    Introduction to Google Cloud Machine Learning Engine course
    Google Cloud Platform account recommended
    This Course Includes
    Many hands-on demos

    Course information :

    Video: .MP4, 1920×1080 30 fps | Audio: AAC, 44.1 kHz, 2ch | Duration: 38m
    Genre: eLearning | Language: English + Subtitles | Size: 675 MB

    Course Link :

    https://cloudacademy.com/course/building-convolutional-neural-networks-on-google-cloud/introduction-302/

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