Deep learning network to visually recognize traffic signs, trained on the German Traffic Sign Benchmark (GTSB).
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Updated
Jan 18, 2017 - HTML
Deep learning network to visually recognize traffic signs, trained on the German Traffic Sign Benchmark (GTSB).
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.
Image augmentation for machine learning experiments.
AIND Term 2 -- Lesson on Convolutional Neural Networks
Term 2 Project 1 Dog Breed Classifier and human face detector using ImageNet, superhuman CNNs, and Haar Cascades
Image augmentation with simultaneous transformation of keypoints, bounding boxes, and segmentation mask
Traffic Sign Classification using Deep Learning on the German Traffic Sign Recognition Benchmark data set.
This is a repository for the code and various numpy files that going along with the face recognition project.
This project is to build a classification model by transfer learning.
Capstone Project for Udacity Machine Learning Nanodegree
Classification of apparel attributes based on images
Using CNN(Keras) and Image Augmentation techniques to classify a given set of handwritten Devnagari characters
A basic keras CNN model Template for Classification Tasks with Image augmentation
Detect a person's mood based on his facial expression using deep learning model
Tensorflow implementation of U-Net for Segmentation of neuronal structures in EM stacks
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