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You will learn to:
Effectively deal with small datasets
Learn about regularization
Decrease overfitting
Perform data augmentation
Skills
Machine Learning Fundamentals
Deep Neural Networks
Prerequisites
Intermediate knowledge of Python
Basics of machine learning
Technologies
Python
Tensorflow
Project Description
Machine learning models need a lot of data to train and adjust their parameters. In the case of small datasets, because of the lack of data, it becomes harder to get better results. This issue may lead to overfitting.
In this project, we’ll be given a Sequential model with all of the boilerplate code. This model has around 95% training with 75% validation accuracy, which shows that the model is overfitted.
Throughout the project, we’ll apply different techniques to reduce overfitting while retaining high accuracy.
Project Tasks
1
Getting Started
Task 0: Introduction
2
Base Model
Task 1: Overview of Predefined Methods
Task 2: Model Results
3
Reducing Overfitting
Task 3: Dropout Layer 1
Task 4: Dropout Layer 2
Task 5: Early Stopping
Task 6: Regularization
Task 7: Data Augmentation
Congratulations
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