Generative AI Content Creation Virtual Internship
In this advanced virtual internship, students will explore the use of generative adversarial networks (GANs) and variational autoencoders (VAEs) to generate synthetic media like images, text, and audio. Through hands-on projects, students will learn how to build and train these models, as well as how to apply them to real-world use cases. Upon completion, students will have a strong understanding of generative AI and the skills to create their own synthetic media.
Track Overview
Tasks & Milestones
Exploring GAN and VAE Architectures
AdvancedIn this task, students will investigate the architectural components and training procedures of GANs and VAEs through hands-on experiments.
Implementing a DCGAN for Image Generation
AdvancedIn this task, students will build and train a deep convolutional GAN (DCGAN) to generate synthetic images.
Implementing a Conditional VAE for Text Generation
AdvancedIn this task, students will build and train a conditional VAE model to generate synthetic text conditioned on specific attributes.
Generating Synthetic Data for Data Augmentation
AdvancedIn this task, students will use generative AI techniques to generate synthetic data for data augmentation, improving the performance of machine learning models.
Prerequisites
- • Intermediate experience in machine learning and deep learning
- • Proficiency in Python programming
- • Familiarity with deep learning frameworks like TensorFlow or PyTorch
Certificate
Certificate of Completion
Earn a certificate upon successful completion