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Advanced Deployment Scenarios with TensorFlow
  • Level Professional
  • المدة 13 ساعات hours
  • الطبع بواسطة DeepLearning.AI
  • Offered by

عن

Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. In this final course, you’ll explore four different scenarios you’ll encounter when deploying models. You’ll be introduced to TensorFlow Serving, a technology that lets you do inference over the web. You’ll move on to TensorFlow Hub, a repository of models that you can use for transfer learning. Then you’ll use TensorBoard to evaluate and understand how your models work, as well as share your model metadata with others. Finally, you’ll explore federated learning and how you can retrain deployed models with user data while maintaining data privacy. This Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

الوحدات

TF serving as another deployment option for the model and ways to install it

    • 5 Videos
    • 3 Readings
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5 Videos

  • Introduction, A conversation with Andrew Ng
  • Introduction
  • Serving
  • Installing TF Serving
  • TensorFlow Serving summary

3 Readings

  • Downloading the Ungraded Labs and Programming Assignments
  • [IMPORTANT] Have questions, issues or ideas? Join our Forum!
  • Installation link

Building a model and deploying to TF Serving

    • 2 Videos
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2 Videos

  • Setup for serving
  • Serving

Passing data to and from the model

    • 4 Videos
    • 1 Readings
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4 Videos

  • Predictions
  • Passing data to serving
  • Getting the predictions back
  • Running the colab

1 Readings

  • TF server running in colab

Looking into a more complex model using the Fashion MNIST dataset

    • 1 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Week 1 Quiz

1 Videos

  • Complex model

1 Readings

  • Serving with Fashion MNIST

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes Week 1

Ungraded Exercise - Serving with MNIST

    • 1 Readings
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1 Readings

  • Ungraded Assignment - Serving with MNIST

TF Hub

    • 5 Videos
    • 3 Readings
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5 Videos

  • Introduction, A conversation with Andrew Ng
  • Introduction to TF Hub
  • Transfer learning
  • Inference
  • Module storage

3 Readings

  • Tensorflow Hub link
  • Link to saved models
  • Colab

Text based models

    • 4 Videos
    • 2 Readings
Show info about module content

4 Videos

  • Text based models
  • Word embeddings
  • Experimenting with embeddings
  • Colab

2 Readings

  • Pre-trained Word Embeddings
  • Text Classification Colab

Image classification

    • 2 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Week 2 Quiz

2 Videos

  • Classify cats and dogs
  • Transfer learning

2 Readings

  • MobileNet model details
  • Colab

Lecture Notes (Optional)

    • 1 Readings
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1 Readings

  • Lecture Notes Week 2

Exercise 2 - TensorFlow Hub

    • 1 Lab
    • 1 Programming
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1 Programming

  • Exercise 2

1 Labs

  • TensorFlow Hub assignment

Overview of Tensorboard

    • 5 Videos
    • 1 Readings
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5 Videos

  • Introduction, A conversation with Andrew Ng
  • Tensorboard scalars
  • Callbacks
  • Histograms
  • Publishing model details

1 Readings

  • tensorboard.dev

Local Tensorboard

    • 1 Videos
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1 Videos

  • Local tensorboard

Graphics and confusion matrix

    • 4 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Week 3 Quiz

4 Videos

  • Looking at graphics in a dataset
  • More than one image
  • Confusion matrix
  • Multiple callbacks

1 Readings

  • Colab

Lecture Notes (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Lecture Notes Week 3

Exercise 3 - Tensorboard

    • 1 Lab
    • 1 Programming
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1 Programming

  • Exercise 3

1 Labs

  • Tensorboard Assignment

Intro to Federated Learning

    • 4 Videos
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4 Videos

  • Introduction, A conversation with Andrew Ng
  • Training on mobile devices
  • Data at the edge
  • How it works

Privacy and masking

    • 2 Videos
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2 Videos

  • Maintaining user privacy
  • Masking

Federated Learning APIs

    • 3 Videos
    • 4 Readings
    • 1 Assignment
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1 Assignment

  • Week 4 Quiz

3 Videos

  • APIs for Federated Learning
  • Example of federated learning
  • Outro

4 Readings

  • Colab
  • [IMPORTANT] Reminder about end of access to Lab Notebooks
  • What next?
  • (Optional) Opportunity to Mentor Other Learners

Lecture Notes (Optional)

    • 1 Readings
Show info about module content

1 Readings

  • Lecture Notes Week 4

Auto Summary

Unlock advanced deployment techniques with TensorFlow in this professional-level course by Coursera. Delve into TensorFlow Serving for web inference, leverage TensorFlow Hub for transfer learning, utilize TensorBoard for model evaluation, and explore federated learning for secure model retraining. Ideal for IT and Computer Science professionals, this 780-minute course builds on prior TensorFlow knowledge. Subscribe with the Starter plan and elevate your machine learning deployment skills.

Laurence Moroney
Instructor

Laurence Moroney