• Computer vision: Lane line detection using Hough Transform • The Perceptron implementation (single neural network) • Model implementation using deep neural network (DNN) • Multiclass classification • Image recognition (MNIST dataset) • Traffic sign detection using convolutional neural network (CNN) • Polynomial regression for model fitting into a 2d continuous dataset • Behavioral Cloning: Data preprocessing, Image preprocessing, NVIDIA Model implementation, Bi-directional client-server architecture, Generator - Augmentation techniques, Batch generator, Fit generator
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RizwanRumi/Self_driving_car
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The purpose of this project for the learning phase to detect lane and traffic signals by applying a deep neural network.
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