Relationship Extraction using a Bi-directional GRU v/s CNN with multiple layers and max-pooling
Project for lecture 5 Neural Networks to "Artificial Intelligence with Python" Harvard course
Deep Convolutional Encoder-Decoder Architecture implemented along with max-pooling indices for pixel-wise semantic segmentation using CamVid dataset.
This project utilizes a CNN model to classify cat and dog images through training and testing processes. The model is created using the Keras library on the TensorFlow backend.
Building a HTTP-accessed convolutional neural network model using TensorFlow NN (tf.nn), CIFAR10 dataset, Python and Flask.
Stock price prediction using LSTM and 1D Convoltional Layer implemented in keras with TF backend on daily closing price of S&P 500 data from Jan 2000 to Aug 2016
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