Autoencoders, transformers, latent space: Learn the elements of generative AI and hear what data scientist David Foster has to say about the potential for generative AI in music, as well as the role that world models play in blending generative AI with reinforcement learning.

This episode is brought to you by Posit, the open-source data science company (https://posit.co), by Anaconda, the world's most popular Python distribution (superdatascience.com/anaconda), and by https://WithFeeling.ai, the company bringing humanity into AI. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• Generative modeling vs discriminative modeling [04:21]
• Generative AI for Music [13:12]
• On the threats of AI [23:15]
• Autoencoders Explained [38:36]
• Noise in Generative AI [48:11]
• What CLIP models are (Contrastive Language-Image Pre-training) [54:07]
• What World Models are [1:00:40]
• What a Transformer is [1:11:14]
• How to use transformers for music generation [1:19:50]

Additional materials: www.superdatascience.com/687

The Super Data Science Podcast: Machine Learning | AI | Success

687: Generative Deep Learning, with David Foster

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast.

As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact.

Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy.

We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

New tracks tagged #Generative Deep Learning