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Super Data Science: ML & AI Podcast with Jon Krohn

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.

Copyright: © 2024 Super Data Science: ML & AI Podcast with Jon Krohn

Latest Episodes:

696: Brain-Computer Interfaces and Neural Decoding, with Prof. Bob Knight Jul 14, 2023

Show notes

Jon Krohn welcomes Professor Dr. Bob Knight to explore human intelligence, the prefrontal cortex, and the transformative potential of brain implants for data collection. Discover the pivotal role of machine learning in treating Parkinson's and delve into exciting future advancements.
Additional materials: www.superdatascience.com/696
Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

695: NLP with Transformers, feat. Hugging Face's Lewis Tunstall Jul 11, 2023

Show notes

What are transformers in AI, and how do they help developers to run LLMs efficiently and accurately? This is a key question in this week’s episode, where Hugging Face’s ML Engineer Lewis Tunstall sits down with host Jon Krohn to discuss encoders and decoders, and the importance of continuing to foster democratic environments like GitHub for creating open-source models.
This episode is brought to you by the AWS Insiders Podcast, by WithFeeling.ai, the company bringing humanity into AI, and by Modelbit, for deploying models in seconds. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.
In this episode you will learn:
• What a transformer is, and why it is so important for NLP [04:34]
• Different types of transformers and how they vary [11:39]
• Why it’s necessary to know how a transformer works [31:52]
• Hugging Face’s role in the application of transformers [57:10]
• Lewis Tunstall’s experience of working at Hugging Face [1:02:08]
• How and where to start with Hugging Face libraries [1:18:27]
• The necessity to democratize ML models in the future [1:25:25]
Additional materials: www.superdatascience.com/695

694: CatBoost: Powerful, efficient ML for large tabular datasets Jul 07, 2023

Show notes

Modeling tabular data and spreadsheets doesn’t have to be tedious with CatBoost’s open-source tree-boosting algorithm. CatBoost does what it says on the tin, blending categories with boosting that allows you to train your models faster and handle large datasets for ML tasks across multiple GPUs. In this week’s Five-Minute Friday, host Jon Krohn gets to grips with the technical components of CatBoost that give it the speed and accuracy so acclaimed by its users.
Additional materials: www.superdatascience.com/694
Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

693: YOLO-NAS: The State of the Art in Machine Vision, with Harpreet Sahota Jul 04, 2023

Show notes

Harpreet Sahota, a data science expert and deep learning developer at Deci AI, joins Jon Krohn to explore the fascinating realm of object detection and the revolutionary YOLO-NAS model architecture. Discover how machine vision models have evolved and the techniques driving compute-efficient edge device applications..
This episode is brought to you by AWS Inferentia, by WithFeeling.ai, the company bringing humanity into AI, and by Modelbit, for deploying models in seconds. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.
In this episode you will learn:
• What is machine vision? [07:02]
• Object detection and YOLO architectures [13:00]
• Deci's YOLO-NAS: Optimal object detection model architecture [23:39]
• Developer Relations [1:00:16]
• Harpreet's 'top-down' approach to learning Deep Learning [1:06:50]
Additional materials: www.superdatascience.com/693

692: Lossless LLM Weight Compression: Run Huge Models on a Single GPU Jun 30, 2023

Show notes

Join Jon as he navigates listeners through the innovative SpQR approach—a cutting-edge, lossless LLM weight compression technique that harnesses the power of quantization. Tune in as Jon delves into the four steps behind this groundbreaking method in this week's episode.
Additional materials: www.superdatascience.com/692
Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

691: A.I. Accelerators: Hardware Specialized for Deep Learning Jun 27, 2023

Show notes

GPUs vs CPUs, chip design and the importance of chips in AI research: This highly technical episode is for anyone who wants to learn what goes into chip development and how to get into the competitive industry of accelerator design. With advice from expert guest Ron Diamant, Senior Principal Engineer at AWS, you’ll get a breakdown of the need-to-know technical terms, what chip engineers need to think about during the design phase and what the future holds for processing hardware.
This episode is brought to you by Posit, the open-source data science company, by the AWS Insiders Podcast, and by 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:
• What CPUs and GPUs are [05:29]
• The differences between accelerators used for deep learning [14:31]
• Trainium and Inferentia: AWS's A.I. Accelerators [22:10]
• If model optimizations will lead to lower demand for hardware to process them [43:14]
• How a chip designer goes about production [48:34]
• Breaking down the technical terminology for chips (accelerator interconnect, dynamic execution, collective communications) [55:29]
• The importance of AWS Neuron, a software development kit [1:15:42]
• How Ron got his foot in the door with chip design [1:26:40]
Additional materials: www.superdatascience.com/691

690: How to Catch and Fix Harmful Generative A.I. Outputs Jun 23, 2023

Show notes

Krishna Gade, the founder and CEO of Fiddler.AI, discusses the challenges faced by Large Language Models (LLMs) in Generative AI, including inaccuracies, biases, and privacy risks. He emphasizes the importance of monitoring to build trust in AI and highlights Fiddler's explainability algorithms and pre-built bias detection tools as vital solutions.
Additional materials: www.superdatascience.com/690
Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

689: Observing LLMs in Production to Automatically Catch Issues Jun 20, 2023

Show notes

Arize's Amber Roberts and Xander Song join Jon Krohn this week, sharing invaluable insights into ML Observability, drift detection, retraining strategies, and the crucial task of ensuring fairness and ethical considerations in AI development.
This episode is brought to you by Posit, the open-source data science company, by AWS Inferentia, and by Anaconda, the world's most popular Python distribution. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.
In this episode you will learn:
• What is ML Observability [05:07]
• What is Drift [08:18]
• The different kinds of model drift [15:31]
• How frequently production models should be retrained? [25:15]
• Arize's open-source product, Phoenix [30:49]
• How ML Observability relates to discovering model biases [50:30]
• Arize case studies [57:13]
• What is a developer advocate [1:04:51]
Additional materials: www.superdatascience.com/689

688: Six Reasons Why Building LLM Products Is Tricky Jun 16, 2023

Show notes

Prompt injection, prompt engineering, context windows, and more: In this week’s Five-Minute Friday, Jon explains why anyone looking to build their own product leveraging LLMs should stop to consider these and three more issues before jumping in. Phillip Carter first outlined these six issues in his article “All the Hard Stuff Nobody Talks About when Building Products with LLMs”.
Additional materials: www.superdatascience.com/688
Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

687: Generative Deep Learning, with David Foster Jun 13, 2023

Show notes

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, by Anaconda, the world's most popular Python distribution, and by 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

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