Prashant Ranjan
PrashantRanjan09
Deep Learning | NLP | Machine Learning
Languages
Repos
21
Stars
343
Forks
80
Top Language
Python
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Top Repositories
Using pre trained word embeddings (Fasttext, Word2Vec)
A short tutorial on Elmo training (Pre trained, Training on new data, Incremental training)
Improving Word Embeddings by combining word embeddings with their POS (Part Of Speech) tag.
Using LIME (Local Interpretable Model-Agnostic Explanations) for text data
Neural Machine Translation using Attention Mechanism
Implementation of the Paper Structured Self-Attentive Sentence Embedding published in ICLR 2017
Repositories
21A short tutorial on Elmo training (Pre trained, Training on new data, Incremental training)
Neural Machine Translation using Attention Mechanism
Using pre trained word embeddings (Fasttext, Word2Vec)
Using LIME (Local Interpretable Model-Agnostic Explanations) for text data
Improving Word Embeddings by combining word embeddings with their POS (Part Of Speech) tag.
Implementation of the Paper Structured Self-Attentive Sentence Embedding published in ICLR 2017
No description provided.
An interactive visualization of your Spotify listening habits. Built with: D3.js, JQuery, Express and Handlebars
Classifying movie reviews as positive or negative using Word2Vec Embeddings & LSTM network
To analyze and remove gender bias in coreference resolution systems
No description provided.
No description provided.
Solutions for Cracking the Coding Interview - 6th Edition
No description provided.
Started as a Team Project for CS690D at UMass Amherst, now turning into pytorch implementation of hyperbolic neural networks using Poincare Ball model.
Source code for the paper "Hyperbolic Neural Networks", https://arxiv.org/abs/1805.09112
TensorFlow code and pre-trained models for BERT
A repository containing 300D character embeddings derived from the GloVe 840B/300D dataset, and uses these embeddings to train a deep learning model to generate Magic: The Gathering cards using Keras
Pre-trained word vectors of 30+ languages
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
Summaries and notes on Deep Learning research papers