LEARNING PATH · AI Engineering
ML Engineering Foundations
For developers moving from app code into machine learning.
The core math and primitives behind modern ML, made runnable: feel how models learn, implement the functions every model relies on, then assemble them into a recommender and a retrieval pipeline.
- Build intuition for how gradient descent trains a model
- Implement softmax, cosine similarity and top-k ranking from scratch
- Connect the primitives to a real recommendation system
- See where retrieval fits in a modern ML stack
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Gradient Descent: The Descent
Play with how a model actually learns.
Softmax
The function behind every classifier head.
Cosine Similarity
How embeddings are compared.
Top-K Retrieval
Rank candidates by similarity.
Design a Recommendation System
Assemble the primitives into a recommender.
Design a RAG Pipeline
Where retrieval fits in a modern stack.