{"id":27195591,"url":"https://github.com/barisyazici/ml_robotics_interview_prep","last_synced_at":"2025-10-04T10:40:28.953Z","repository":{"id":284700132,"uuid":"955304508","full_name":"BarisYazici/ml_robotics_interview_prep","owner":"BarisYazici","description":"Machine learning robotics engineer preparation material. 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[linear_algebra_ml_cheat_sheet.md](linear_algebra_ml_cheat_sheet.md)\n   - Vector spaces, transformations, eigenvalues\n   - Rotations, translations, homogeneous coordinates\n\n2. **Robotics Foundations** - [robotics_cheat_sheet.md](robotics_cheat_sheet.md)\n   - Kinematics and dynamics\n   - Control theory, PID controllers\n   - Robot perception basics\n\n3. **Sensor Fusion** - [3d_scanning_advanced_cheatsheet.md](3d_scanning_advanced_cheatsheet.md)\n   - Multi-sensor calibration\n   - Visual-inertial systems\n   - Kalman filters and particle filters\n\n4. **SLAM \u0026 Navigation** - [visual_slanm/visual_slam.md](visual_slanm/visual_slam.md), [3d_scanning_advanced_cheatsheet.md](3d_scanning_advanced_cheatsheet.md)\n   - Visual SLAM approaches\n   - Loop closure\n   - Path planning\n\n### Computer Vision \u0026 3D Geometry Path\n1. **Image Processing Basics** - [comprehesive_robotics_ml.md](comprehesive_robotics_ml.md)\n   - Filtering, feature detection\n   - Camera models and calibration\n\n2. **3D Representations** - [3d_scanning_advanced_cheatsheet.md](3d_scanning_advanced_cheatsheet.md)\n   - Point clouds, meshes, implicit surfaces\n   - Neural radiance fields (NeRF)\n   - Conversion between representations\n\n3. **3D Reconstruction** - [3d_geometry.md](3d_geometry.md)\n   - Structure from Motion (SfM)\n   - Multi-view stereo\n   - TSDF fusion\n\n4. **Advanced Topics** - [3d_scanning_advanced_cheatsheet.md](3d_scanning_advanced_cheatsheet.md)\n   - Non-rigid registration\n   - Neural implicit representations\n   - Physics-based reconstruction\n\n### Machine Learning Path\n1. **ML Fundamentals** - [ml_interview_book_summary.md](ml_interview_book_summary.md)\n   - Supervised vs. unsupervised learning\n   - Model evaluation and validation\n   - Optimization methods\n\n2. **Deep Learning** - [andrew_ng_deep_learning_interview_prep.md](andrew_ng_deep_learning_interview_prep.md)\n   - Neural network architectures\n   - Training techniques\n   - Regularization and optimization\n\n3. **Recurrent Neural Networks** - [ml_interview_questions/dropout_rnn.html](ml_interview_questions/dropout_rnn.html)\n   - RNN architectures\n   - LSTM and GRU\n   - Dropout techniques in RNNs\n\n4. **Computer Vision \u0026 ML** - [comprehesive_robotics_ml.md](comprehesive_robotics_ml.md)\n   - CNNs for vision tasks\n   - Object detection and segmentation\n   - Neural rendering\n\n## Topic-Specific Guides\n\n### SLAM\n- Core concepts: [visual_slanm/visual_slam.md](visual_slanm/visual_slam.md)\n- Advanced techniques: [visual_slanm/slam.md](visual_slanm/slam.md)\n- Integration with other systems: [3d_scanning_advanced_cheatsheet.md#x-sensor-fusion-integration-framework](3d_scanning_advanced_cheatsheet.md#x-sensor-fusion-integration-framework)\n\n### Deep Learning\n- RNN dropout visualization: [ml_interview_questions/dropout_rnn.html](ml_interview_questions/dropout_rnn.html)\n- Advanced architectures: [andrew_ng_deep_learning_interview_prep.md](andrew_ng_deep_learning_interview_prep.md)\n- Training techniques: [ml_interview_book_answers.md](ml_interview_book_answers.md)\n\n### 3D Geometry\n- Representations: [3d_scanning_advanced_cheatsheet.md#i-integrated-approach-to-3d-representations](3d_scanning_advanced_cheatsheet.md#i-integrated-approach-to-3d-representations)\n- Reconstruction: [3d_scanning_cheatsheet.md](3d_scanning_cheatsheet.md)\n- Advanced optimization: [3d_scanning_advanced_cheatsheet.md#ix-advanced-optimization-methods-integration](3d_scanning_advanced_cheatsheet.md#ix-advanced-optimization-methods-integration)\n\n### Robotics\n- Fundamentals: [robotics_cheat_sheet.md](robotics_cheat_sheet.md)\n- Motion planning: [comprehesive_robotics_ml.md](comprehesive_robotics_ml.md)\n- Control: [comprehesive_robotics_ml.md](comprehesive_robotics_ml.md)\n\n## Interactive Visualizations\n\nThis repository includes interactive visualizations to help understand complex concepts:\n\n- **RNN Dropout**: [ml_interview_questions/dropout_rnn.html](ml_interview_questions/dropout_rnn.html) - Visualize how standard and variational dropout work in recurrent neural networks\n- **Bayesian Neural Networks**: [bayes_neural_networks.html](system_diagrams/bayes_neural_networks.html) - Understand uncertainty in neural networks\n\n## Interview Preparation\n\n- **Common Questions**: Each cheatsheet includes a section with interview questions related to that topic\n- **System Design Questions**: See the \"Complex System Design\" sections in advanced cheatsheets\n- **Coding Challenges**: [pytorch_cheatsheet.md](pytorch_cheatsheet.md) contains practical examples and exercises\n\n## Contributing\n\nFeel free to contribute by adding new resources, fixing errors, or improving existing materials. Submit a pull request with your changes.\n\n## License\n\nApache Version 2.0 - See [LICENSE](LICENSE) file for details\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbarisyazici%2Fml_robotics_interview_prep","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbarisyazici%2Fml_robotics_interview_prep","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbarisyazici%2Fml_robotics_interview_prep/lists"}