Learning

Modules

1. Math for DL
2. Neural nets in NumPy
3. PyTorch fluency
4. Regularization + optimization
5. Convnets + vision
6. RNNs, embeddings
7. Transformers from scratch
8. Tokenization + scaling laws
9. Pretraining your foundation model
10. Fine-tuning + alignment (LoRA/QLoRA/DPO)
11. Efficient inference (quant / vLLM / spec decoding)
12. Multimodal + agents + RAG + capstone

End state

• Rebuild nanoGPT line by line
• Pretrain a 100M-param LLM for ~$3
• SFT + LoRA/QLoRA fine-tune
• INT4 quantize + serve with vLLM

Modules

1. Setup & Tools
2. Python Foundations
3. Math Foundations
4. DS & Algos
5. Databases & SQL
6. Data Engineering
7. Backend & APIs
8. Systems & Infrastructure
9. Machine Learning
10. LLMs & Modern AI
11. MLOps & Production
12. System Design + Capstone

End state

• Full-stack fluency top to bottom
• Ship 3 capstone projects
• Read prod code without flinching
• Interview-ready for L4/L5 IC roles

Modules

1. How to read a problem (UMPIRE)
2. Complexity + Python fluency
3. Arrays, hashing, two pointers
4. Sliding window + prefix sums
5. Binary search + sorting
6. Linked lists, stacks, queues
7. Trees + BSTs
8. Graphs (BFS/DFS/topo/union-find)
9. Heaps, intervals, greedy
10. Dynamic programming
11. Backtracking + advanced patterns
12. Mock loops + company-tagged prep

End state

• Recognise the pattern before you code
• Explain complexity out loud, every time
• Own the follow-up they escalate to
• Run a timed mock without freezing

Modules

1. Developer tooling
2. Web fundamentals
3. C# language + .NET runtime
4. ASP.NET Core
5. React + TypeScript
6. Frontend build & state
7. Auth (Google OAuth PKCE)
8. Data + EF Core + Postgres
9. Azure App Service + Front Door
10. Testing (xunit + Playwright)
11. Security + observability
12. Shipping workflow

End state

• Ship a .NET 8 + React app to Azure
• Google OAuth PKCE, from scratch
• Postgres migrations that don't lie
• Front Door + CI/CD wired up

Modules

1. Where LLMs came from
2. How transformers attend
3. RAG architecture basics
4. Prompting for depth
5. Generating audio
6. Medical NLP case study
7. Stanford CS229 notes
8. Azure AI Foundry

End state

• Explain attention from first principles
• Design a RAG pipeline
• Write prompts that hold up
• Ship an LLM app on Azure

Modules

1. Kafka 101 for ML
2. Apache Airflow in depth
3. Azure Data Explorer / Kusto
4. Data infra for experimentation
5. Engineering clarity deep dive

End state

• Reason about streaming vs batch
• Orchestrate real pipelines
• Query at scale with Kusto
• Design experimentation infra

Modules

1. Design from first signals
2. A worked build
3. Production engine teardown

End state

• Model user/item signals
• Build candidate + ranking stages
• Read a real recsys architecture

Modules

1. Scripts → API on Azure
2. Docker deployment on Azure
3. Windows app → store
4. A learning system that sticks

End state

• Deploy a script as an API
• Containerise + ship on Azure
• Publish a desktop app
• Build a durable study loop

Modules

1. Requirements → architecture
2. Estimation that constrains
3. Storage decision tree
4. CAP and consistency
5. Six classic designs
6. Real-time analytics + CDC
7. Lakehouse and metering
8. Feature store + model serving
9. RAG and eval gates
10. Two full design reviews

End state

• Derive an architecture from constraints
• Estimate well enough to rule options out
• Defend a storage choice under questioning
• Run an end-to-end design review