About this episode
Dec 22
When Data Stops Being Code and Starts Being Conversation (Ep. 297)
Mark Brocato built Mockaroo—the tool that taught millions of developers how to fake data. Now, as Head of Engineering at Tonic.ai, he's building the AI agent that's making his own creation obsolete. In this episode, we explore why static test data can't survive the AI era, what i ... Show More
33m 37s
Nov 25
Your AI Strategy is Burning Money: Here's How to Fix It (Ep.295)
<p>Most companies don't have an AI problem. They have a decision-making problem. Matt Lea, founder of Schematical and CloudWarGames, has spent nearly 20 years helping tech leaders ship smarter.</p>
<p>In this conversation, he breaks down when AI actually makes sense, where AWS co ... Show More
31m 58s
Nov 11
From Tokens to Vectors: The Efficiency Hack That Could Save AI (Ep. 294)
<p>LLMs generate text painfully slow, one low-info token at a time. Researchers just figured out how to compress 4 tokens into smart vectors & cut costs by 44%—with full code & proofs! Meanwhile OpenAI drops product ads, not papers.
We explore CALM & why open scienc ... Show More
46m 54s
Apr 2021
464: A.I. vs Machine Learning vs Deep Learning
In this episode, I tackle three often conflated terms - AI, machine learning, and deep learning - to shine some light on what exactly they are.
Additional materials: www.superdatascience.com/464
7m 14s
Feb 2017
MLG 001 Introduction
<p>Show notes: <a href= "https://ocdevel.com/mlg/1?utm_source=podcast&utm_medium=mlg&utm_campaign=mlg1" target="_blank" rel="noopener">ocdevel.com/mlg/1</a>. MLG teaches the fundamentals of machine learning and artificial intelligence. It covers intuition, models, math, languages ... Show More
8m 11s
Apr 2017
Feature Processing for Text Analytics
It seems like every day there's more and more machine learning problems that involve learning on text data, but text itself makes for fairly lousy inputs to machine learning algorithms. That's why there are text vectorization algorithms, which re-format text data so it's ready f ... Show More
17m 28s
Feb 2017
MLG 004 Algorithms - Intuition
<div> <p>Machine learning consists of three steps: prediction, error evaluation, and learning, implemented by training algorithms on large datasets to build models that can make decisions or classifications. The primary categories of machine learning algorithms are supervised, un ... Show More
23m 27s
Mar 2017
MLG 010 Languages & Frameworks
44m 36s
Jun 2024
SE Radio 622: Wolf Vollprecht on Python Tooling in Rust
<p><strong>Wolf Vollprecht</strong>, the CEO and founder of Prefix.dev, speaks with host Gregory M. Kapfhammer about how to implement Python tools, such as package managers, in the Rust programming language. They discuss the challenges associated with building Python infrastructu ... Show More
55m 10s
Jul 2023
AI Today Podcast: AI Glossary Series – Automated Machine Learning (AutoML)
In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the term Automated Machine Learning (AutoML), explain how this term relate to AI and why it’s important to know about them.
Show Notes:
FREE Intro to CPMAI mini course
CPMAI Training and Certifi ... Show More
9m 11s
Jul 2023
AI Today Podcast: AI Glossary Series – Data Science Notebooks, Jupyter, Colab
In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Data Science Notebooks, Jupyter, Colab, explain how these terms relate to AI and why it’s important to know about them.
Show Notes:
FREE Intro to CPMAI mini course
CPMAI Training and C ... Show More
11 m
Mar 2017
MLG 009 Deep Learning
51m 28s
Nov 2024
SE Radio 641: Catherine Nelson on Machine Learning in Data Science
<p><strong>Catherine Nelson</strong>, author of the new O'Reilly book, <em data-renderer-mark="true">Software Engineering for Data Scientists</em>, discusses the collaboration between data scientists and software engineers -- an increasingly common pairing on machine learning and ... Show More
48m 19s
Also on YouTube
Two AI experts who actually love the technology explain why chasing AGI might be the worst thing for AI's future—and why the current hype cycle could kill the field we're trying to save. Want to dive deeper? Head to datascienceathome.com for detailed show notes, code examples, and exclusive deep-dives into the papers we discuss.
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<p><a href= "https://ocdevel.com/walk?utm_source=podcast&utm_medium=mlg&utm_campaign=mlg10"> Try a walking desk</a> to stay healthy while you study or work!</p> <p>Full notes at <a href= "https://ocdevel.com/mlg/10?utm_source=podcast&utm_medium=mlg&utm_campaign=mlg10"> ocdevel.c ... Show More
<p><a href= "https://ocdevel.com/walk?utm_source=podcast&utm_medium=mlg&utm_campaign=mlg9"> Try a walking desk</a> to stay healthy while you study or work!</p> <p>Full notes at <a href= "htts://ocdevel.com/mlg/9?utm_source=podcast&utm_medium=mlg&utm_campaign=mlg9"> ocdevel.com/ml ... Show More
