Master Generative Deep Learning: GANs & VAEs in Python
What you will learn:
- Master the principles of generative models in deep learning.
- Construct variational autoencoders using Theano and TensorFlow.
- Build generative adversarial networks (GANs) from scratch with Theano and TensorFlow.
- Gain a foundational understanding of AI technologies like OpenAI's ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion.
- Develop practical skills to create AI applications that generate new data.
Description
Dive deep into the world of generative deep learning and unlock the power of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). This comprehensive course empowers you to create groundbreaking AI applications like image generation, text synthesis, and more.
Learn from the ground up how these powerful techniques work under the hood, building practical models using Python, Theano, and TensorFlow. Discover the fundamental concepts behind unsupervised learning and explore the connections to cutting-edge technologies like OpenAI's DALL-E, Midjourney, and Stable Diffusion.
Through hands-on exercises and detailed explanations, you'll master the art of building GANs and VAEs from scratch. This course goes beyond simple library calls, equipping you with a deep understanding of the underlying algorithms and their implementation.
Key Highlights:
- Unravel the mysteries of unsupervised learning and its potential for generating new data.
- Develop a strong foundation in GANs and VAEs, understanding their strengths and weaknesses.
- Gain hands-on experience building generative models with Python, Theano, and TensorFlow.
- Explore the relationship between these techniques and real-world AI applications like image generation and text synthesis.
Join us and unlock the potential of generative deep learning. Enroll now and embark on a journey to build the next generation of AI models.