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 # ML Frameworks
 
-coming soon.
+## Introduction
+
+- Definition of ML Frameworks
+- What is an embedded ML framework?
+- Why are embedded ML frameworks important?
+- Challenges of embedded ML
+- Benefits of using embedded ML frameworks, trade-offs and differences.
+
+## Typical ML Frameworks
+
+- TensorFlow, PyTorch, Keras, ONNX Runtime, Scikit-learn
+- Key Features and Advantages
+- API and Programming Paradigms
+
+## Constraints for Embedded AI
+
+### Hardware
+
+- Memory Usage
+- Processing Power
+- Energy Efficiency
+- Storage Limitations
+- Hardware diversity
+
+### Software
+
+- Library Dependency
+- Lack of OS
+
+## Embedded AI Frameworks
+
+- TensorFlow Lite
+- ONNX Runtime
+- MicroPython
+- CMSIS-NN
+- Edge Impulse
+- Others (mentioning briefly some less common but significant frameworks)
+
+## Framework Comparison
+
+- Table of differences and similarities
+
+## Toolchain Integration
+
+- Compatibility with Embedded Development Environments
+- Integration with Firmware and Hardware
+
+## Trends in ML Frameworks
+
+- Framework Developments on the Horizon
+- Anticipated Innovations in the Field
+
+## Conclusion
+
+- Summary of Key Takeaways
+- Recommendations for Further Learning
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