Tutorials

1. C
2. C++
3. CUDA
4. ARM Neon architecture
5. C#
6. SQL
7. Dart

Subjects

1. Languages
   a. C++
   b. C
   c. CUDA
   d. Python
2. DSA
   a. Data structures
   b. Algorithms
3. Computer Vision
   a. Edge detectors
   b. Features detector and descriptor
   c. Line detection
   d. Image processing
   e. Frequency domain feature detector
   f. Fish eye distortion correction
   g. Distance estimation
   h. Camera calibration
   i. Top view generation
   j. Camera-LiDAR calibration
   k. Thresholding techniques
   l. Image enhancement techniques
   m. RANSAC
   n. Epipolar geometry
   o. Optical flow
   p. Image enhancement techniques q. Floating to fixed point conversion
4. DL/ML
   a. Basics
   b. PCA
   c. SVM
   d. DBSCAN
   e. Clustering methods
5. Kalman Filter
   a. Linear filter
   b. Non-linear filters
   c. Early fusion
   e. Late fusion
6. ROS
7. Docker
8. Algebra
   a. Basics
   b. PNP Transformation.
9. Projects
   a. Smart Parking
   b. LDW: Curved lane detection
   c. BEV
   d. Calibration: Camera, Camera-LiDAR
10. OS
11. ML
12. Maths
    a. Permutation and combination
    b. Probability
    c. Algebra
13. Sensors:
    a. Camera
    b. LiDAR
    c. RADAR

Projects

1. Smart parking
2. LDW: Curved lane detection
3. BEV
4. Calibration: Camera, Camera-LiDAR
5. Distance estimation

Courses

1. M. Tech courses.
2. Thinkautonomous courses.
3. Udemy courses:
   a. ROS
4. Coursera
   a. Robotics
   b. Deeplearning.ai

Resources

1. Geometric transformations: https://pages.mtu.edu/~shene/COURSES/cs3621/NOTES/geometry/geo-tran.html
2. Think Autonomous github: https://github.com/Jeremy26?tab=repositories
3. CUDA sample code github: https://github.com/CodedK/CUDA-by-Example-source-code-for-the-book-s-examples-/tree/master