Building Deep Learning Applications with Keras 2.0

Description

In this course, you will learn:

• In this course, learn how to install Keras and use it to build a simple deep learning model. Explore the many powerful pre-trained deep learning models included in Keras and how to use them.
• Discover how to deploy Keras models, and how to transfer data between Keras and TensorFlow so that you can take advantage of all the TensorFlow tools while using Keras.
• When you wrap up this course, you'll be ready to start building and deploying your own models with Keras.

Syllabus:

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

1. Keras Overview

• What is Keras?
• TensorFlow and Theano backends
• Using Keras vs. TensorFlow
   

2. Setting Up Keras

• Installing Keras with the TensorFlow backend on macOS
• Installing Keras with the TensorFlow backend on Windows
   

3. Creating a Neural Network in Keras

• The train-test-evaluation flow
• Keras Sequential API
• Pre-processing training data
• Define a Keras model using the Sequential API
   

4. Training Models

• Training and evaluating the model
• Making predictions
• Saving and loading models
   

5. Pre-Trained Models in Keras

• Pre-trained models
• Recognize images with ResNet50 model
   

6. Monitoring a Keras model with TensorBoard

• Export Keras logs in TensorFlow format
• Visualize the computational graph
• Visualize training progress
   

7. Using a Trained Keras Model in Google Cloud

• Exporting Google Cloud-compatible models
• Configuring a new Google Cloud account
• Uploading a Keras model to Google Cloud
• Using a model in Google Cloud

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