Vanishing Gradients
a data podcast with hugo bowne-anderson
Displaying Episode 21 - 30 of 30 in total of Vanishing Gradients with the tag “machine learning”.
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Episode 32: Building Reliable and Robust ML/AI Pipelines
July 27th, 2024 | Season 1 | 1 hr 15 mins
data science, genai, llms, machine learning
Hugo speaks with Shreya Shankar, a researcher at UC Berkeley focusing on data management systems with a human-centered approach. Shreya's work is at the cutting edge of human-computer interaction (HCI) and AI, particularly in the realm of large language models (LLMs). Her impressive background includes being the first ML engineer at Viaduct, doing research engineering at Google Brain, and software engineering at Facebook.
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Episode 31: Rethinking Data Science, Machine Learning, and AI
July 9th, 2024 | Season 1 | 1 hr 36 mins
data science, genai, llms, machine learning
Hugo speaks with Vincent Warmerdam, a senior data professional and machine learning engineer at :probabl, the exclusive brand operator of scikit-learn. Vincent is known for challenging common assumptions and exploring innovative approaches in data science and machine learning.
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Episode 28: Beyond Supervised Learning: The Rise of In-Context Learning with LLMs
June 10th, 2024 | Season 1 | 1 hr 5 mins
data science, genai, llms, machine learning
Hugo speaks with Alan Nichol, co-founder and CTO of Rasa, where they build software to enable developers to create enterprise-grade conversational AI and chatbot systems across industries like telcos, healthcare, fintech, and government.
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Episode 27: How to Build Terrible AI Systems
May 31st, 2024 | Season 1 | 1 hr 32 mins
data science, genai, llms, machine learning
Hugo speaks with Jason Liu, an independent consultant who uses his expertise in recommendation systems to help fast-growing startups build out their RAG applications. He was previously at Meta and Stitch Fix is also the creator of Instructor, Flight, and an ML and data science educator.
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Episode 26: Developing and Training LLMs From Scratch
May 15th, 2024 | Season 1 | 1 hr 51 mins
data science, genai, machine learning
Hugo speaks with Sebastian Raschka, a machine learning & AI researcher, programmer, and author.They’ll tell you everything you need to know about LLMs, but were too afraid to ask: from covering the entire LLM lifecycle, what type of skills you need to work with them, what type of resources and hardware, prompt engineering vs fine-tuning vs RAG, how to build an LLM from scratch, and much more.
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Episode 25: Fully Reproducible ML & AI Workflows
March 18th, 2024 | Season 1 | 1 hr 20 mins
data science, genai, machine learning
Hugo speaks with Omoju Miller, a machine learning guru and founder and CEO of Fimio, where she is building 21st century dev tooling.
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Episode 24: LLM and GenAI Accessibility
February 27th, 2024 | Season 1 | 1 hr 30 mins
data science, genai, machine learning
Hugo speaks with Johno Whitaker, a Data Scientist/AI Researcher doing R&D with answer.ai, about where we’ve come from regarding tooling and accessibility for foundation models, ML, and AI, where we are, and where we’re going.
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Episode 18: Research Data Science in Biotech
May 25th, 2023 | Season 1 | 1 hr 12 mins
ai, biotech, data science, machine learning, open source, python
Machine learning, deep learning, Bayesian inference for drug discovery, OSS, and accelerating discovery science to the speed of thought!
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Episode 7: The Evolution of Python for Data Science
May 2nd, 2022 | Season 1 | 1 hr 2 mins
ai, data science, machine learning, python
Hugo speaks with Peter Wang, CEO of Anaconda, about how Python became so big in data science, machine learning, and AI. They jump into many of the technical and sociological beginnings of Python being used for data science, a history of PyData, the conda distribution, and NUMFOCUS.
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Episode 5: Executive Data Science
March 23rd, 2022 | Season 1 | 1 hr 48 mins
ai, data science, machine learning
Hugo speaks with Jim Savage, the Director of Data Science at Schmidt Futures, about the need for data science in executive training and decision, what data scientists can learn from economists, the perils of "data for good", and why you should always be integrating your loss function over your posterior.