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Building Deep Learning Applications with Keras 2.0

via LinkedIn Learning

Overview

Learn how to install Keras— a popular deep learning framework—and use it to build a simple deep learning model.

Keras is a popular programming framework for deep learning that simplifies the process of building deep learning applications. Instead of providing all the functionality itself, it uses either TensorFlow or Theano behind the scenes and adds a standard, simplified programming interface on top. 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
  • Welcome
  • 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
Conclusion
  • Next steps

Taught by

Adam Geitgey

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