Building Deep Learning Applications with Keras 2.0

Get a thorough introduction to Keras, a versatile deep learning framework, and learn how to build, deploy, and monitor robust deep learning models.

Introduction

• Reshaping the world with deep learning
• Essential background and knowledge
• How to use Codespaces and the exercise files

1. Understanding Keras

• Understanding deep learning and Keras
• Neuron as we know it
• Exploring the TensorFlow and Theano backends
• Distinction between Keras and TensorFlow

2. Setting up Keras

• Keras installation with a TensorFlow backend on Windows

3. Getting Started with Keras Models

• The Train-Test-Evaluate cycle
• Introduction to the Keras Sequential API
• Data pre-processing for training
• Building a model using the Sequential API

4. Model Training and Performance Analysis

• Training models
• Model predictions and evaluation
• Visualize results and save the model

5. Leveraging Pre-Trained Models in Keras

• Exploring pre-trained models
• Image recognition with the ResNet50 model

6. Tools for Visualization and Assessment

• Exporting logs for Keras to TensorFlow
• Monitoring training performance with TensorBoard
• Visualizing computation graphs with TensorBoard

Conclusion

• Next steps