Meet the Video Game Designer Who’s Leveling Up with AI
December 20, 2023
Article
Recommended experience
Intermediate level
At least one year of experience in using deep learning frameworks such as TensorFlow and Keras in Python
Recommended experience
Intermediate level
At least one year of experience in using deep learning frameworks such as TensorFlow and Keras in Python
Clean and preprocess data for GANs
Employ GANs (Generative Adversarial Networks) for data generation
Apply PCA (Principal Component Analysis) for Data Exploration
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Only available on desktop
In this 2-hour guided project, you will learn how to leverage Generative AI for data generation to address data imbalance. SecureTrust Financial Services, a financial institution, has asked us to help them improve the accuracy of their fraud detection system. The model is a binary classifier, but it's not performing well due to data imbalance. As data scientists, we will employ Generative Adversarial Networks (GANs), a subset of Generative AI, to create synthetic fraudulent transactions that closely resemble real transactions. This approach aims to balance the dataset and enhance the accuracy of the fraud detection model.
This guided project is designed for those interested in learning how Generative models can increase model accuracy by generating synthetic data. To make the most of this project, it is recommended to have at least one year of experience using deep learning frameworks such as TensorFlow and Keras in Python.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Load the Dataset
Preprocess and Explore the data
Create the Generator model
Practice Task - Data Preprocessing for Neural Networks
Create the Discriminator model
Combine Generator and Discriminator models to Build The GAN
Train and evaluate our GAN
Generate synthetic data using the trained Generator
Challenge Task - Principal Component Analysis for Data visualization
At least one year of experience in using deep learning frameworks such as TensorFlow and Keras in Python
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Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
Course
Coursera Instructor Network
Course
Johns Hopkins University
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Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.