https://github.com/barisyazici/ml_robotics_interview_prep
Machine learning robotics engineer preparation material.
https://github.com/barisyazici/ml_robotics_interview_prep
3d-scanning behavior-cloning cnn computer-vision diffusion ml-system-design ml-system-diagrams motion-capture reinforcement-learning rnn robotics robotics-control slam transformers
Last synced: 9 months ago
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Machine learning robotics engineer preparation material.
- Host: GitHub
- URL: https://github.com/barisyazici/ml_robotics_interview_prep
- Owner: BarisYazici
- License: apache-2.0
- Created: 2025-03-26T12:40:26.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-27T07:27:13.000Z (over 1 year ago)
- Last Synced: 2025-04-09T19:51:07.184Z (about 1 year ago)
- Topics: 3d-scanning, behavior-cloning, cnn, computer-vision, diffusion, ml-system-design, ml-system-diagrams, motion-capture, reinforcement-learning, rnn, robotics, robotics-control, slam, transformers
- Language: HTML
- Homepage:
- Size: 12.9 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ML & Robotics Interview Preparation
A comprehensive collection of resources for machine learning, computer vision, robotics, and 3D geometry interview preparation.
## Repository Overview
This repository contains cheatsheets, interactive visualizations, and reference materials to help you prepare for technical interviews in ML, robotics, and related fields.
## Learning Paths
### Robotics Path
1. **Linear Algebra Fundamentals** - [linear_algebra_ml_cheat_sheet.md](linear_algebra_ml_cheat_sheet.md)
- Vector spaces, transformations, eigenvalues
- Rotations, translations, homogeneous coordinates
2. **Robotics Foundations** - [robotics_cheat_sheet.md](robotics_cheat_sheet.md)
- Kinematics and dynamics
- Control theory, PID controllers
- Robot perception basics
3. **Sensor Fusion** - [3d_scanning_advanced_cheatsheet.md](3d_scanning_advanced_cheatsheet.md)
- Multi-sensor calibration
- Visual-inertial systems
- Kalman filters and particle filters
4. **SLAM & Navigation** - [visual_slanm/visual_slam.md](visual_slanm/visual_slam.md), [3d_scanning_advanced_cheatsheet.md](3d_scanning_advanced_cheatsheet.md)
- Visual SLAM approaches
- Loop closure
- Path planning
### Computer Vision & 3D Geometry Path
1. **Image Processing Basics** - [comprehesive_robotics_ml.md](comprehesive_robotics_ml.md)
- Filtering, feature detection
- Camera models and calibration
2. **3D Representations** - [3d_scanning_advanced_cheatsheet.md](3d_scanning_advanced_cheatsheet.md)
- Point clouds, meshes, implicit surfaces
- Neural radiance fields (NeRF)
- Conversion between representations
3. **3D Reconstruction** - [3d_geometry.md](3d_geometry.md)
- Structure from Motion (SfM)
- Multi-view stereo
- TSDF fusion
4. **Advanced Topics** - [3d_scanning_advanced_cheatsheet.md](3d_scanning_advanced_cheatsheet.md)
- Non-rigid registration
- Neural implicit representations
- Physics-based reconstruction
### Machine Learning Path
1. **ML Fundamentals** - [ml_interview_book_summary.md](ml_interview_book_summary.md)
- Supervised vs. unsupervised learning
- Model evaluation and validation
- Optimization methods
2. **Deep Learning** - [andrew_ng_deep_learning_interview_prep.md](andrew_ng_deep_learning_interview_prep.md)
- Neural network architectures
- Training techniques
- Regularization and optimization
3. **Recurrent Neural Networks** - [ml_interview_questions/dropout_rnn.html](ml_interview_questions/dropout_rnn.html)
- RNN architectures
- LSTM and GRU
- Dropout techniques in RNNs
4. **Computer Vision & ML** - [comprehesive_robotics_ml.md](comprehesive_robotics_ml.md)
- CNNs for vision tasks
- Object detection and segmentation
- Neural rendering
## Topic-Specific Guides
### SLAM
- Core concepts: [visual_slanm/visual_slam.md](visual_slanm/visual_slam.md)
- Advanced techniques: [visual_slanm/slam.md](visual_slanm/slam.md)
- Integration with other systems: [3d_scanning_advanced_cheatsheet.md#x-sensor-fusion-integration-framework](3d_scanning_advanced_cheatsheet.md#x-sensor-fusion-integration-framework)
### Deep Learning
- RNN dropout visualization: [ml_interview_questions/dropout_rnn.html](ml_interview_questions/dropout_rnn.html)
- Advanced architectures: [andrew_ng_deep_learning_interview_prep.md](andrew_ng_deep_learning_interview_prep.md)
- Training techniques: [ml_interview_book_answers.md](ml_interview_book_answers.md)
### 3D Geometry
- Representations: [3d_scanning_advanced_cheatsheet.md#i-integrated-approach-to-3d-representations](3d_scanning_advanced_cheatsheet.md#i-integrated-approach-to-3d-representations)
- Reconstruction: [3d_scanning_cheatsheet.md](3d_scanning_cheatsheet.md)
- Advanced optimization: [3d_scanning_advanced_cheatsheet.md#ix-advanced-optimization-methods-integration](3d_scanning_advanced_cheatsheet.md#ix-advanced-optimization-methods-integration)
### Robotics
- Fundamentals: [robotics_cheat_sheet.md](robotics_cheat_sheet.md)
- Motion planning: [comprehesive_robotics_ml.md](comprehesive_robotics_ml.md)
- Control: [comprehesive_robotics_ml.md](comprehesive_robotics_ml.md)
## Interactive Visualizations
This repository includes interactive visualizations to help understand complex concepts:
- **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
- **Bayesian Neural Networks**: [bayes_neural_networks.html](system_diagrams/bayes_neural_networks.html) - Understand uncertainty in neural networks
## Interview Preparation
- **Common Questions**: Each cheatsheet includes a section with interview questions related to that topic
- **System Design Questions**: See the "Complex System Design" sections in advanced cheatsheets
- **Coding Challenges**: [pytorch_cheatsheet.md](pytorch_cheatsheet.md) contains practical examples and exercises
## Contributing
Feel free to contribute by adding new resources, fixing errors, or improving existing materials. Submit a pull request with your changes.
## License
Apache Version 2.0 - See [LICENSE](LICENSE) file for details