Building and Deploying Deep Learning Applications with TensorFlow

Discover how to install TensorFlow and use it to create, train, and deploy a machine learning model.

TensorFlow is one of the most popular deep learning frameworks available. It's used for everything from cutting-edge machine learning research to building new features for the hottest start-ups in Silicon Valley. In this course, learn how to install TensorFlow and use it to build a simple deep learning model. After he shows how to get TensorFlow up and running, instructor Adam Geitgey demonstrates how to create and train a machine learning model, as well as how to leverage visualization tools to analyze and improve your model. Finally, he explains how to deploy models locally or in the cloud. When you wrap up this course, you'll be ready to start building and deploying your own models with TensorFlow.

Introduction

• Welcome
• What you should know
• Using the exercise files

1. Setting Up TensorFlow

• Install TensorFlow on macOS
• Install TensorFlow on Windows

2. TensorFlow Overview

• What is TensorFlow?
• Why is it called TensorFlow?
• Hardware, software, and language requirements
• The train/test/evaluation flow in TensorFlow
• Build a simple model in TensorFlow

3. Creating a TensorFlow Model

• Options for loading data
• Load the data set
• Define the model structure
• Set up the model training loop

4. Training a Model in TensorFlow

• Train
• Log
• Save and load trained models

5. TensorBoard

• Visualize the computational graph
• Visualize training runs
• Add custom visualizations to TensorBoard

6. Using a Trained TensorFlow

• Export models for use in production
• Configure a new Google Cloud account
• Host your model in the cloud with Google Cloud
• Use a model in the cloud

Conclusion

• Next steps

Introduction

• Welcome
• What you should know
• Using the exercise files

1. Setting Up TensorFlow

• Install TensorFlow on macOS
• Install TensorFlow on Windows

2. TensorFlow Overview

• What is TensorFlow?
• Why is it called TensorFlow?
• Hardware, software, and language requirements
• The train/test/evaluation flow in TensorFlow
• Build a simple model in TensorFlow

3. Creating a TensorFlow Model

• Options for loading data
• Load the data set
• Define the model structure
• Set up the model training loop

4. Training a Model in TensorFlow

• Train
• Log
• Save and load trained models

5. TensorBoard

• Visualize the computational graph
• Visualize training runs
• Add custom visualizations to TensorBoard

6. Using a Trained TensorFlow

• Export models for use in production
• Configure a new Google Cloud account
• Host your model in the cloud with Google Cloud
• Use a model in the cloud

Conclusion

• Next steps