Building and Deploying Deep Learning Applications with TensorFlow

Description

In this course, you will learn:

• how to install TensorFlow and use it to build a simple deep learning model
• 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.
• how to deploy models locally or in the cloud.

Syllabus:

• Introduction
• 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

 

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