Pinned Repositories
slack-chat-stats
ads_minor_project
CNN_image_classification
Trained a CNN model to fit the CIFAR-10 dataset and train using the AlexNet Architecture. The experiment also trains with different Normalization techniques such as Batch Normalization & L2
factualityeval
gene_NER_tagging
Training a CRF model to perform Named Entity Recognition to tag gene data.
ISL-Communicator-using-CNN
Indian Sign Language Communicator which recognises hand gestures and saves speech output.
LSTM_hyperparameter_tuning
Used the salesforce awd_lstm state of the art model to tune hyperparameters and compare performance for Language Modeling
MLP
Training a multi layer perceptron on a dataset containing mobile prices and features to classify the price of the mobile.
sentiment_analysis_hotel_reviews
Sentiment Analysis of hotel reviews using training data of positive and negative hotel reviews using 5 features: Positive review count, Negative Review Count, Occurrence of word 'no', Pronoun count, exclamation count.
sreenivasarvind's Repositories
sreenivasarvind/LSTM_hyperparameter_tuning
Used the salesforce awd_lstm state of the art model to tune hyperparameters and compare performance for Language Modeling
sreenivasarvind/ads_minor_project
sreenivasarvind/CNN_image_classification
Trained a CNN model to fit the CIFAR-10 dataset and train using the AlexNet Architecture. The experiment also trains with different Normalization techniques such as Batch Normalization & L2
sreenivasarvind/factualityeval
sreenivasarvind/gene_NER_tagging
Training a CRF model to perform Named Entity Recognition to tag gene data.
sreenivasarvind/ISL-Communicator-using-CNN
Indian Sign Language Communicator which recognises hand gestures and saves speech output.
sreenivasarvind/MLP
Training a multi layer perceptron on a dataset containing mobile prices and features to classify the price of the mobile.
sreenivasarvind/sentiment_analysis_hotel_reviews
Sentiment Analysis of hotel reviews using training data of positive and negative hotel reviews using 5 features: Positive review count, Negative Review Count, Occurrence of word 'no', Pronoun count, exclamation count.