ML Concepts
- Introduction to ML
- Framing
- Descending into ML
- Reducing Loss
- First Steps with TF
- Generalization
- Training and Test Sets
- Validation Set
- Representation
- Feature Crosses
- Regularization: Simplicity
- Logistic Regression
- Classification
- Regularization: Sparsity
- Neural Networks
- Training Neural Nets
- Multi-Class Neural Nets
- Embeddings
ML Engineering
- Production ML Systems
- Static vs. Dynamic Training
- Static vs. Dynamic Inference
- Data Dependencies
- Fairness
ML Systems in the Real World
- Cancer Prediction
- Literature
- Guidelines
Machine Learning Crash Course.
Google.
developers.google.com .
2020.