Course Outline
Introduction
Overview of Neural Networks for Government Applications
Understanding Convolutional Networks in Public Sector Contexts
Setting up Keras for Government Use
Overview of Keras Features and Architecture for Government Projects
Overview of Keras Syntax for Government Developers
Understanding How a Keras Model Organizes Layers for Government Applications
Configuring the Keras Backend (TensorFlow or Theano) for Government Systems
Implementing an Unsupervised Learning Model for Government Data Analysis
Analyzing Images with a Convolutional Neural Network (CNN) for Government Operations
Preprocessing Data for Government Models
Training the Model for Government Use Cases
Training on CPU vs GPU vs TPU for Government Systems
Evaluating the Model for Government Performance Metrics
Using a Pre-trained Deep Learning Model for Government Tasks
Setting up a Recurrent Neural Network (RNN) for Government Applications
Debugging the Model for Government Projects
Saving the Model for Government Deployment
Deploying the Model for Government Operations
Monitoring a Keras Model with TensorBoard for Government Oversight
Troubleshooting for Government Users
Summary and Conclusion for Government Applications
Requirements
- Proficiency in Python programming.
- Familiarity with the Linux command line environment.
Intended Audience for Government
- Software developers
- Data scientists
