Modeling the Iris Dataset with TensorFlow
Explore the famous Iris dataset in our advanced TensorFlow course. Learn to preprocess data, build, and train a multi-class classifier. Evaluate performance with metrics and visualizations. Conclude with model optimization techniques to boost efficiency and accuracy, and cover saving/loading for deployment.
Lessons and practices
Exploring and Preprocessing the Iris Dataset
Changing Train-Test Split Ratio
Fix the Data Preprocessing Bugs
Hands-on Data Preprocessing
End-to-end Preprocessing the Iris Dataset
Multi-Class Model Training Basics
Changing Training Parameters in TensorFlow
Fixing TensorFlow Model Training
Building a TensorFlow Model
Implementation of a TensorFlow Model
Understanding Model Performance Evaluation
Visualizing Accuracy for Model Evaluation
Fixing Bugs in TensorFlow Evaluation
Evaluate Model Accuracy and Loss
Visualizing Model Performance and Evaluation
Early Stop on Training with TensorFlow
Modify Early Stopping Parameters
Fix TensorFlow Early Stopping Code
Initialize Early Stopping Callback
Implement Early Stopping in TensorFlow
Model Saving and Loading Basics with TensorFlow
Changing Saved Model's Name
Fix Model Saving and Loading
Implementing Save and Load in TensorFlow
Save, Load, and Verify Models
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