[Code] | [Report]

Implemented and modified NVIDIA's End to end learning for self driving cars paper and used a deep neural network to mimic driving behavior.

[Code] | [Report]

Built and trained a deep neural network to classify traffic signs, using TensorFlow. Experimented with different network architectures. Performed image pre-processing and validation to guard against overfitting.

[Code] | [Report]

Detected highway lane lines on a video stream. Used OpencV image analysis techniques to identify lines, including Hough Transforms and Canny edge detection.

[Code]

The project is aimed at implementing a two-layered convolutional neural network using a purely functional approach.

Used an extended kalman filter to estimate the state of a moving object of interest with noisy lidar and radar measurements.

[Code]

The project takes an RGB image as input and detects and counts the number of cylinders(circles) present in the image and generates a CSV report.

The project was conducted in partnership with PayTM to help the local villagers in and around Pilani to go cashless at the time of demonetisation.