The success of a machine learning model depends on several factors and events. True generalization to data that the model has never seen before is more a chimera than a reality. But under specific conditions a well trained machine learning model can generalize well and perform with testing accuracy that is similar to the one performed during training.
In this episode I explain when and why machine learning models fail from training to testing datasets.
Rust in the Cosmos Part 2: testing software in space (Ep. 255)
Rust in the Cosmos: Decoding Communication Part I (Ep. 254)
AI and Video Game Development: Navigating the Future Frontier (Ep. 253)
Kaggle Kommando's Data Disco: Laughing our Way Through AI Trends (Ep. 252)
Revolutionizing Robotics: Embracing Low-Code Solutions (Ep. 251)
Is SQream the fastest big data platform? (Ep. 250)
OpenAI CEO Shake-up: Decoding December 2023 (Ep. 249)
Careers, Skills, and the Evolution of AI (Ep. 248)
Open Source Revolution: AI’s Redemption in Data Science (Ep. 247)
Money, Cryptocurrencies, and AI: Exploring the Future of Finance with Chris Skinner [RB] (Ep. 246)
Debunking AGI Hype and Embracing Reality [RB] (Ep. 245)
Destroy your toaster before it kills you. Drama at OpenAI and other stories (Ep. 244)
The AI Chip Chat 🤖💻 (Ep. 243)
Rolling the Dice: Engineering in an Uncertain World (Ep. 242)
How Language Models Are the Ultimate Database(Ep. 241)
Elon is right this time: Rust is the language of AI (Ep. 240)
Attacking LLMs for fun and profit (Ep. 239)
Unlocking Language Models: The Power of Prompt Engineering (Ep. 238)
Erosion of Software Architecture Quality in the Age of AI Code Generation (Ep. 237)
The new dimension of AI: Vector Databases (Ep. 236)
Create your
podcast in
minutes
It is Free
Insight Story: Tech Trends Unpacked
Zero-Shot
Fast Forward by Tomorrow Unlocked: Tech past, tech future
Black Wolf Feed (Chapo Premium Feed Bootleg)
Bannon`s War Room