Free course alert for anyone tired of agents that forget everything the second a session closes. That's the pain Oracle and DEEPLEARNINGAI are targeting with their new Adaptive Agents masterclass, and the curriculum comes from Nacho Martínez and Casius Lee working with AndrewYNg and DeepLearning.AI. What makes it land is the pacing. In roughly an hour, three heavy adaptation layers get architected from the ground up. Behavior layer: agent traces become reusable skills, and a human approval gate makes sure only verified logic gets through. Knowledge layer: your codebase gets mapped through a knowledge graph stitched together from imports and historical Git co-edits, which demonstrates the retrieval advantages graphs have over basic keyword search. Model layer: you learn to spot the exact points at which fine-tuning and weight-space approaches become required for your stack. You come away with a full grasp of how to make agents improve measurably with every run. Free course, about an hour, all three layers.
51m
Free course alert for anyone tired of agents that forget everything the second a session closes. That's the pain Oracle and DEEPLEARNINGAI are targeting with their new Adaptive Agents masterclass, and the curriculum comes from Nacho Martínez and Casius Lee working with AndrewYNg and DeepLearning.AI. What makes it land is the pacing. In roughly an hour, three heavy adaptation layers get architected from the ground up. Behavior layer: agent traces become reusable skills, and a human approval gate makes sure only verified logic gets through. Knowledge layer: your codebase gets mapped through a knowledge graph stitched together from imports and historical Git co-edits, which demonstrates the retrieval advantages graphs have over basic keyword search. Model layer: you learn to spot the exact points at which fine-tuning and weight-space approaches become required for your stack. You come away with a full grasp of how to make agents improve measurably with every run. Free course, about an hour, all three layers.
51m
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