MLG 004 Algorithms - Intuition

The 2025 generative AI image market is a trade-off between aesthetic quality, instruction-following, and user control. This episode analyzes the key platforms, comparing Midjourney's artistic output against the superior text generation and prompt adherence of GPT-4o and Imagen 4, ... Show More

Auto encoders are neural networks that compress data into a smaller "code," enabling dimensionality reduction, data cleaning, and lossy compression by reconstructing original inputs from this code. Advanced auto encoder types, such as denoising, sparse, and variational auto encod ... Show More

At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup ... Show More

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

In this episode I make a non exhaustive list of machine learning tools and frameworks, written in Rust. Not all of them are mature enough for production environments. I believe that community effort can change this very quickly. To make a comparison with the Python ecosystem I wi ... Show More

Bryan Catanzaro, vice president for applied deep learning research at NVIDIA, talks about how we know an AI technology is working, the potential for AI-powered speech, and where we’ll see the next deep learning breakthroughs. 

Machine learning algorithms need examples of data from which they can learn, especially supervised machine learning algorithms. However, one big challenge for those looking to put machine learning into practice is the lack of a sufficient quantity of good quality data examples fr ... Show More

Enjoy this special encore episode. A programming technique where the developer doesn't specify each step of the algorithm in code, but instead teaches the algorithm to learn from the experience. 

Catherine Nelson, author of the new O’Reilly book, Software Engineering for Data Scientists, discusses the collaboration between data scientists and software engineers -- an increasingly common pairing on machine learning and AI projects. Host Philip Winston speaks with Nelson ab ... Show More

Sal Khan, the founder and CEO of Khan Academy, thinks artificial intelligence could spark the greatest positive transformation education has ever seen. This week we're revisiting a talk where he shares the opportunities he sees for students and educators to collaborate with AI to ... Show More

Send us a textEpisode 300: Navigating AI and Human Cognition In this special 300th episode of My EdTech Life, I sit down with Benjamin Riley, founder of Cognitive Resonance, to explore the intersection of AI and human cognition in education. We discuss everything from the hype su ... Show More

Paola Jaramillo is working at MathWorks as the Technical Manager for the Application Engineering team supporting you and your team of domain experts in the vision creation and implementation of innovative technologies with leading tools and services for professional software deve ... Show More

Thanks to machine learning and artificial intelligence, programs that used to take a week can now quickly search 50,000 IP addresses for vulnerabilities in organisations like yours to get into. Reformed Hacker and Brand Ambassador for CTRL Group Bastien Treptel speaks with Associ ... Show More