Residual_Neural_Network-master.zip_jubernotebook_mysteriousglc_r

# Residual_Neural_Network The Residual Neural Network implementation with the use of Keras neural network layers , Te network includes one Convolution block to filter out the feature required by model and Identity Feedback to train on Convolution features. Residual Networks are very deep networks in Neural network mainly used in retrieving the complex alignments in data. Residual Networks mainly used in the Image analysis to learn and abstract the features of different levels in edges. The Deeper Networks always works in learning high end features. Here The Residual network is implemented in doing classification problem. The Residual Network is given with very less 0.04% of data because of computation constraints.