Machine Learning with TensorFlow & Cloud ML

Google Cloud 2018    |    Intermediate
  • 14 videos | 1h 19m 47s
  • Includes Assessment
  • Earns a Badge
Likes 60 Likes 60
Cloud ML combines the Google Cloud Platform with TensorFlow to create models at scale. Explore concepts behind TensorFlow and scaling, as well as training models locally and in the cloud.

WHAT YOU WILL LEARN

  • describe concepts of machine learning in relation to GCP
    describe the use of datasets in GCP
    demonstrate how to load a dataset for Cloud ML in GCP
    describe the use of TensorFlow with machine learning
    run a TensorFlow Python program in Google Cloud Shell
    use TensorFlow to run a local trainer
    demonstrate how to use TensorBoard to inspect TensorFlow logs and graphs
  • run a local trainer in distributed mode
    demonstrate how to run a single-instance trainer in the cloud
    inspect Stackdriver logs for an ML Engine job run in the cloud
    describe the process of scaling with Cloud ML
    demonstrate how to run distributed training in the cloud
    use hyperparameter tuning to help maximize a model's predictive accuracy
    describe the TensorFlow operations that are used for big data

IN THIS COURSE

  • 3m 48s
    Upon completion of this video, you will be able to describe machine learning concepts in relation to GCP. FREE ACCESS
  • 3m 19s
    Upon completion of this video, you will be able to describe the use of datasets in Google Cloud Platform. FREE ACCESS
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    3.  Loading Datasets for Cloud ML in GCP
    4m 57s
    In this video, you will learn how to load a dataset for Cloud ML in Google Cloud Platform. FREE ACCESS
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    4.  Understanding TensorFlow
    2m 40s
    After completing this video, you will be able to describe the use of TensorFlow for machine learning. FREE ACCESS
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    5.  Running a TensorFlow Python Program
    4m 56s
    In this video, find out how to run a TensorFlow Python program on Google Cloud Shell. FREE ACCESS
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    6.  Using TensorFlow to Run a Local Trainer
    10m 7s
    In this video, find out how to use TensorFlow to run a local training session. FREE ACCESS
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    7.  Using TensorBoard to Inspect Logs
    7m 2s
    In this video, you will learn how to use TensorBoard to inspect TensorFlow logs and graphs. FREE ACCESS
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    8.  Running Local Trainers in Distributed Mode
    4m 49s
    In this video, learn how to run a local trainer in distributed mode. FREE ACCESS
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    9.  Running a Single-instance Trainer in the Cloud
    11m 34s
    In this video, you will learn how to run a single-instance trainer in the cloud. FREE ACCESS
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    10.  Inspecting Stackdriver Logs
    4m 11s
    In this video, you will inspect Stackdriver logs for an ML Engine job run in the cloud. FREE ACCESS
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    11.  Understanding Cloud ML Scaling
    2m 23s
    After completing this video, you will be able to describe the process of scaling with Cloud ML Engine. FREE ACCESS
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    12.  Running Distributed Training in the Cloud
    4m 54s
    In this video, you will learn how to run distributed training in the cloud. FREE ACCESS
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    13.  Using Hyperparameter Tuning
    5m 13s
    In this video, you will learn how to use hyperparameter tuning to help maximize a model's predictive accuracy. FREE ACCESS
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    14.  Exercise: Using TensorFlow and Cloud ML
    9m 52s
    Upon completion of this video, you will be able to describe the TensorFlow operations used for big data. FREE ACCESS

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