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recurrent_neural_networks_to_predict_churn
Predicting Customer Churn using past customer behavior data is a approach which is more dependent on the preprocessing of the data and how you are approach that. Since lots of data is sparse and requires lots of hand engineered features.Then feeding them in a machine learning model. Since the data is in a time series format, therefore I tried to use Recurrent Neural Networks (LSTM cell unit). But as I said this might not be the best approach unless the data is huge since a deep learning model will not be able to outperform a traditional machine learning algorithm unless the dataset is extremely large and there is minimum sparseness in the time series sequence of the data.text_summarizer
Applied attention on Sequence to Sequence model for the task of Text Summarizer using Tensorflow's raw_rnn. here, I haven't used Tensorflow's inbuilt seq2seq function. The reason behind is to apply attention mechanism manually.Dynamic-Coattention-Network
Implementation of Dynamic Coattention Network for Question Answeringdiabetic_retinopathy_new
PlayPongWithPolicyGradients
Atari Pong from pixels using Policy Gradientsdiabetic_retinopathy
Convolutional Neural Networks to classify between normal and affected retina from fundus imagesurl_shortener
URL Shortener FLASK based - Running on HerokuSiamese_LSTM_basic
Implementation of Siamese LSTM for Semantic similaritybasic_tutorial_tensorflow
Tensorflow Tutorials Basics created by Sayon Duttacnn_assignment_dl_nd_udacity
CNN Assignment of Deep Learning Nanodegreehiggs_boson
Kaggle Higgs Boson ChallengeGAN_face_generation
Udacity GAN Assignmentdeep_learning_tasks
Experimenting with different Deep Learning approaches. <will update accordingly>rnn_assignment_dl_nd_udacity
katacoda-scenarios
Katacoda Scenariostesting
Testing some GIT featurestestbot
udacity_rl_quadcopter
Udacity Final Project using Deep Reinforcement Learningsayondutta.github.io
datasciencecoursera
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