Google’s Nested Learning work reframes every modern AI system—LLMs, memory stacks, even optimizers—as a nest of interacting learning loops that compress context at different timescales.¹² Today’s post consolidates the two study plans I sketched earlier and turns them into a single, chunkable roadmap you can follow to build that kind of multi-layer AI “brain.” Each layer below includes three tracks: Theory for conceptual depth, Code for hands-on intuition, and Scale for production-level reality.

Why Nested Learning Matters Right Now

Nested Learning argues that models should manage fast and slow memories, adapt their own optimizers, and learn policies that can rewrite every layer on the fly.¹² Instead of treating “foundation model,” “memory,” and “deployment” as separate checkboxes, we learn to stack them. The roadmap mirrors that stack: start with calculus-level ML theory, then move through deep learning, LLMs, robotics interfaces, and finally MLOps so the whole hierarchy can run unattended.

Layer 1 — Foundations (Math + Core ML)

Theory: Stanford CS229 delivers the statistical spine—bias/variance, regularization, SVMs, probabilistic models, and a first glimpse at policy optimization.³ The lectures make the later Nested Learning math (multi-level optimization, memory compression) feel natural instead of mystical.

Code: DataCamp’s Supervised/Unsupervised Learning with scikit-learn forces you to implement regressors, classifiers, and clustering pipelines on real datasets.⁴ You’ll tune hyperparameters, compare metrics, and feel why “small, purposeful models” still win when latency and cost matter.

Scale: Google AI Essentials is a short, pragmatic tour of generative AI tools, prompt engineering, and responsible AI guardrails.⁵ Think of it as the user-facing perspective that reminds you who you’re actually building for.

Layer 2 — Deep Learning & Explainability

Theory: MIT 6.S191 condenses neural networks, backpropagation math, and modern generative topics (including LLMs) into a fast-paced bootcamp.⁶ It’s perfect prep for reasoning about what each nested loop is actually optimizing.

Code: DataCamp’s Introduction to Deep Learning with PyTorch walks through training loops, custom modules, and debugging gradients.⁷ Writing your own training scripts makes the nested-memory story concrete—you feel every optimizer step.

Scale: Google’s Intro to Vertex AI Studio gives you an end-to-end lab on prompt design, tuning Gemini models, and deploying multi-modal apps.⁸ It’s the bridge between a single notebook and a resilient service.

Layer 3 — LLM Stack + Reinforcement Learning

Theory: Stanford CS224N dives deep into transformers, attention, and the research frontier of large language models.⁹ By the end you can explain how context compression and self-attention echo the Nested Learning idea of multi-scale memory.

Code: DataCamp’s Developing Large Language Models track pairs PyTorch with Hugging Face so you can fine-tune and evaluate open models.¹⁰ You’ll experiment with tokenization, adapters, and evaluation harnesses—all prerequisites for RLHF and nested policy tuning.

Reinforcement Learning Track: DataCamp’s Reinforcement Learning in Python brings you from MDPs and Q-learning up to deep RL and RLHF workflows.¹¹ This is where nested loops become explicit: you train reward models that, in turn, train the base model.

Layer 4 — Robotics, Spatial Interfaces, and Edge Context

Nested Learning isn’t limited to text; the paper highlights memory banks that update at different speeds, which maps nicely onto robots collecting streaming context while the cloud updates slower policies.¹²

Theory: NVIDIA’s Robotics Fundamentals path covers kinematics, sensing, and ROS2, grounding you in how robots perceive and act.¹²

Code: Use Isaac Sim or ROS labs from that same path to script sensor fusion, perception stacks, and teleoperation routines—great sandboxes for testing nested memory ideas.

Scale: Coursera’s Introduction to Robotics on AWS shows how to pair robots with cloud orchestration, secure comms, and simulation at scale.¹³ Treat it as the glue between your edge robot and cloud “slow brain.”

Layer 5 — Production, MLOps, and Cloud Deployment

Code & Process: DataCamp’s MLOps Concepts course walks through the pipeline from experiment tracking and feature stores to CI/CD and monitoring.¹⁴ Here you learn how to keep each nested learner versioned, tested, and revertable.

Scale: Google’s Build Real World AI Apps with Gemini & Imagen badge demonstrates how to package multi-modal models on Vertex AI and ship them to users.¹⁵ It’s the culmination of the stack: prompt-to-product with observability, governance, and deploy buttons that anyone on the team can trust.

How to Work the Roadmap

1. Chunk in Sprints: Alternate theory and code tracks every week so the math never drifts too far from the keyboard. For example, pair a CS229 lecture with the corresponding scikit-learn lab.
2. Nest Your Own Projects: Treat each layer as a service in a larger system. Build a small LLM agent, wrap it in an RLHF loop, then deploy it via the MLOps coursework. The minor friction of wiring them together is the whole point.
3. Add Memory Gradually: Start with basic vector stores, then experiment with slower “reflection” passes after you complete the RL course. The Nested Learning idea is to keep stacking optimizers and memories until the system feels alive.
4. Simulate Before Hardware: The robotics layer emphasizes simulation (Isaac Sim, AWS RoboMaker) so you can prototype multi-timescale control without blowing a budget or a motor.
5. Ship Every Month: Use the Vertex AI and Gemini/Imagen labs to ship something small monthly—a prompt tool, a captioning XR demo, a robot monitoring dashboard—so deployment becomes muscle memory.

Closing the Loop

By sequencing the roadmap this way, we end up with a personal “nested learner”: fast loops for coding and testing, medium loops for week-long projects, and slow loops for strategy and ethics. Keep the references below handy; they’re the canonical docs and courses that shaped this plan. Next up for me is running the robotics labs side-by-side with RLHF experiments so I can prototype a world-aware memory system before the year closes.

References

Footnotes

1. Behrouz et al., “Nested Learning: The Illusion of Deep Learning Architectures” (NeurIPS 2025). ↩ ↩² ↩³

2. Google Research, “Introducing Nested Learning: A New ML Paradigm for Continual Learning.” ↩ ↩² ↩³

3. Stanford CS229: Machine Learning. ↩

4. DataCamp, “Supervised Learning with scikit-learn.” ↩

5. Google, “AI Essentials.” ↩

6. MIT 6.S191: Introduction to Deep Learning. ↩

7. DataCamp, “Introduction to Deep Learning with PyTorch.” ↩

8. Google Skills, “Introduction to Vertex AI Studio.” ↩

9. Stanford CS224N: Natural Language Processing with Deep Learning. ↩

10. DataCamp, “Developing Large Language Models.” ↩

11. DataCamp, “Reinforcement Learning in Python.” ↩

12. NVIDIA, “Robotics Fundamentals Learning Path.” ↩

13. Coursera, “Introduction to Robotics on AWS.” ↩

14. DataCamp, “MLOps Concepts.” ↩

15. Google Skills, “Build Real World AI Applications with Gemini & Imagen.” ↩