Generative AI is revolutionizing how we create — turning neural networks into powerful tools for art, music, storytelling, and synthesis. In this course, you’ll harness the full potential of generative deep learning by building models that produce realistic images, lifelike voices, original music, and human-quality text.

We’ll begin with the fundamentals of generative modeling: latent space, sampling techniques, and loss functions. Then, you’ll implement powerful architectures like Variational Autoencoders (VAEs), GANs (Generative Adversarial Networks), and diffusion models, progressing from theory to hands-on coding.

Through guided labs and creative assignments, you’ll train your own models to generate portraits, compose melodies, mimic voices, and write stories — all using Python, TensorFlow, and PyTorch. You’ll also learn how to fine-tune pre-trained models, build prompt-driven generative systems, and apply AI art responsibly with an understanding of ethics, bias, and copyright.

If you’re ready to merge creativity with code and explore what deep learning can truly invent, this is your blueprint.

Delivery
Courses are delivered 100% online. Learn on your schedule — videos, case studies, and templates are available instantly upon enrollment. All content is optimized for mobile and desktop.

Refunds
Request a full refund within 30 days if the course doesn’t meet your expectations in applied generative modeling.

Language
English

Curriculum Tab
Module 1: Foundations of Generative Modeling – Latent space, noise sampling, loss functions.
Module 2: GANs & VAEs in Practice – Training, evaluation, creative outputs in vision/audio.
Module 3: Text, Audio & Diffusion – Transformer-based generation, audio mimicry, stable diffusion.
Module 4: Ethics, Deployment & Future Trends – Bias, copyright, tuning models for safe deployment.
Capstone Creative Lab: Build a generative AI demo that outputs original content (e.g., short video, song clip, illustrated story, or chatbot).