Pinned Repositories
clinicalBERT
repository for Publicly Available Clinical BERT Embeddings
deep-learning-repo
Implementation of Deep Learning architectures in TensorFlow and Keras
demand-price-estimation
Dynamic pricing for flights based on demand.
elements-of-programming
This repository includes my python solutions to some standard algorithms and data structure problems.
enron-nlp-mining
Text analysis on Enron emails data
hackerearth
hierarchical-labelled-clustering-evaluation
Evaluation of GCFI++ algorithm and system using Reuters dataset
kaggle
Few of my kernels to solve DS problems at Kaggle.
modality_tests
Various python tests in to identify and describe multi-modal data distributions.
nlp-with-deep-learning
Stanford course cs224d - nlp with deep learning
vinidixit's Repositories
vinidixit/enron-nlp-mining
Text analysis on Enron emails data
vinidixit/demand-price-estimation
Dynamic pricing for flights based on demand.
vinidixit/hierarchical-labelled-clustering-evaluation
Evaluation of GCFI++ algorithm and system using Reuters dataset
vinidixit/modality_tests
Various python tests in to identify and describe multi-modal data distributions.
vinidixit/clinicalBERT
repository for Publicly Available Clinical BERT Embeddings
vinidixit/deep-learning-repo
Implementation of Deep Learning architectures in TensorFlow and Keras
vinidixit/elements-of-programming
This repository includes my python solutions to some standard algorithms and data structure problems.
vinidixit/hackerearth
vinidixit/kaggle
Few of my kernels to solve DS problems at Kaggle.
vinidixit/nlp-with-deep-learning
Stanford course cs224d - nlp with deep learning
vinidixit/hierarchical-labelled-clustering
vinidixit/interviewbit
vinidixit/leetcode
vinidixit/scikit-learn
scikit-learn: machine learning in Python
vinidixit/stanford-machine-learning-course
This repository consists of my Matlab codes for ML algorithms implementations. It consists of various coding assignments done while attending ML course by Andrew Ng. It consists of vectorized implementation of various cost functions, optimizers, gradient updates and finding best parameters for the decision boundary.
vinidixit/Surprise
A Python scikit for building and analyzing recommender systems