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

   • Vector spaces, transformations, eigenvalues
   • Rotations, translations, homogeneous coordinates

2. Robotics Foundations - robotics_cheat_sheet.md

   • Kinematics and dynamics
   • Control theory, PID controllers
   • Robot perception basics

3. Sensor Fusion - 3d_scanning_advanced_cheatsheet.md

   • Multi-sensor calibration
   • Visual-inertial systems
   • Kalman filters and particle filters

4. SLAM & Navigation - visual_slanm/visual_slam.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

   • Filtering, feature detection
   • Camera models and calibration

2. 3D Representations - 3d_scanning_advanced_cheatsheet.md

   • Point clouds, meshes, implicit surfaces
   • Neural radiance fields (NeRF)
   • Conversion between representations

3. 3D Reconstruction - 3d_geometry.md

   • Structure from Motion (SfM)
   • Multi-view stereo
   • TSDF fusion

4. Advanced Topics - 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

   • Supervised vs. unsupervised learning
   • Model evaluation and validation
   • Optimization methods

2. Deep Learning - 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

   • RNN architectures
   • LSTM and GRU
   • Dropout techniques in RNNs

4. Computer Vision & ML - 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
• Advanced techniques: visual_slanm/slam.md
• Integration with other systems: 3d_scanning_advanced_cheatsheet.md#x-sensor-fusion-integration-framework

Deep Learning

• RNN dropout visualization: ml_interview_questions/dropout_rnn.html
• Advanced architectures: andrew_ng_deep_learning_interview_prep.md
• Training techniques: ml_interview_book_answers.md

3D Geometry

• Representations: 3d_scanning_advanced_cheatsheet.md#i-integrated-approach-to-3d-representations
• Reconstruction: 3d_scanning_cheatsheet.md
• Advanced optimization: 3d_scanning_advanced_cheatsheet.md#ix-advanced-optimization-methods-integration

Robotics

• Fundamentals: robotics_cheat_sheet.md
• Motion planning: comprehesive_robotics_ml.md
• Control: comprehesive_robotics_ml.md

Interactive Visualizations

This repository includes interactive visualizations to help understand complex concepts:

• RNN Dropout: ml_interview_questions/dropout_rnn.html - Visualize how standard and variational dropout work in recurrent neural networks
• Bayesian Neural Networks: 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 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 file for details