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.
Chatting with ChatGPT: Pros and Cons of Advanced Language AI (Ep. 215)
Accelerating Perception Development with Synthetic Data (Ep. 214)
Edge AI applications for military and space [RB] (Ep. 213)
From image to 3D model (Ep. 212)
Machine learning is physics (Ep. 211)
Autonomous cars cannot drive. Here is why. (Ep. 210)
Evolution of data platforms (Ep. 209)
[RB] Is studying AI in academia a waste of time? (Ep. 208)
Private machine learning done right (Ep. 207)
Edge AI for applications in military and space (Ep. 206)
[RB] What are generalist agents and why they can change the AI game (Ep. 205)
LIDAR, cameras and autonomous vehicles (Ep. 204)
Predicting Out Of Memory Kill events with Machine Learning (Ep. 203)
Is studying AI in academia a waste of time? (Ep. 202)
Zero-Cost Proxies: How to find the best neural network without training (Ep. 201)
Online learning is better than batch, right? Wrong! (Ep. 200)
What are generalist agents and why they can change the AI game (Ep. 199)
Streaming data with ease. With Chip Kent from Deephaven Data Labs (Ep. 198)
Learning from data to create personalized experiences with Matt Swalley from Omneky (Ep. 197)
State of Artificial Intelligence 2022 (Ep. 196)
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