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Modeling the Iris Dataset with TensorFlow

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Overview

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.

Syllabus

Unit 1: Preprocessing the Iris Dataset for TensorFlow

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

Unit 2: Building a Multi-Class Classification Model with TensorFlow

Multi-Class Model Training Basics
Changing Training Parameters in TensorFlow
Fixing TensorFlow Model Training
Building a TensorFlow Model
Implementation of a TensorFlow Model

Unit 3: Deep Evaluation of Model Performance

Understanding Model Performance Evaluation
Visualizing Accuracy for Model Evaluation
Fixing Bugs in TensorFlow Evaluation
Evaluate Model Accuracy and Loss
Visualizing Model Performance and Evaluation

Unit 4: Implementing Early Stopping in TensorFlow to Prevent Overfitting

Early Stop on Training with TensorFlow
Modify Early Stopping Parameters
Fix TensorFlow Early Stopping Code
Initialize Early Stopping Callback
Implement Early Stopping in TensorFlow

Unit 5: Saving and Loading a TensorFlow Model

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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