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Created almost 8 years ago
Updated about 7 years ago

wolfapple

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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.

traffic-sign-recognition

Built and trained a deep neural network to classify traffic signs, using PyTorch. The highlights of this solution would be data preprocessing, trained with heavily augmented data and using Spatial Transformer Network.

Jupyter Notebook

CarND-Behavioral-Cloning-Project

Built and trained a convolutional neural network for end-to-end driving in a simulator, using TensorFlow and Keras. Used optimization techniques such as regularization and dropout to generalize the network for driving on multiple tracks.

Python

resty_atp

Ruby

CarND-Vehicle-Detection

Detect and track vehicles using color and gradient features and a support vector machine classifier.

Jupyter Notebook

CarND-Semantic-Segmentation

Labeled the pixels of a road in images using a Fully Convolutional Network (FCN).

Python

wolfapple/CarND-Traffic-Sign-Classifier-Project

wolfapple

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