DNN, CNN and RNN in tensorflow.. DNN and CNN have upto 99% test accuracy.. The trained RNN(GRU) generates random Shakespeare plays...
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Updated
Mar 25, 2018 - Python
DNN, CNN and RNN in tensorflow.. DNN and CNN have upto 99% test accuracy.. The trained RNN(GRU) generates random Shakespeare plays...
Collaborating Filtering Project
Deep Learning class projects from Kagle. All projects are individual projects conducted by me using pyhton (keras, tensor-flow, matplotlib and other libraries). Different Deep Neural Network (DNN) methods were used and results were compared based one efficiency and accuracy. Results and conclusions based on results were reported.
DRAW aims to extract effective inferences from online drug reviews that would benefit drug users, pharma companies, and clinicians by receiving feedback on the drug based on opinion mining.
RNN-LSTM model that classifies movie reviews
Tensorflow ile Türkçe cümle analizi.
The model is character-based, for each character the model looks up the embedding, runs the GRU one timestep with the embedding as input, and applies the dense layer to generate logits predicting the log-likelihood of the next character.
The goal of this project is to predict traffic using data collected from sensors. The prediction of traffic can be implemented in many ways that will improve traffic management, safety, and designing infrastructure.
[TGRS21] Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations
POS tagging through Recurrent Neural Architectures
This project is meant to be a plug and play template, for anyone looking to build a univariate forecasting model using LSTM, GRU or RNN
Click below to checkout the website
decompilation and static-analysis on the prevalent hermeticwiper
The objective of this project was to construct LSTM and GRU binary classifier models that could predict the class of input enzyme sequences.
Language modeling using nairaland featured links as dataset
Recognition of million drawings doodle considering three types of birds: 'duck', 'flamingo' and 'swan'. The project focused on six kind of models: MLP, LSTM, GRU, bi-LSTM, CNN and CNN-D
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