Using LSTMs to crack ciphers such as caesar cipher, vigenere cipher and enigma
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Updated
Nov 28, 2019 - Jupyter Notebook
Using LSTMs to crack ciphers such as caesar cipher, vigenere cipher and enigma
Music Prediction Project for the CS 6355 Structured Prediction Spring 2021 class at the University of Utah
Includes examples of code I have written both independently and collaboratively.
The idea is to develop a machine learning program to identify when an article might be fake news.
Predicting missing metadata with recurrent neural network (RNNs) based entity extraction
Uses deep learning to translate Indian Sign Language in real-time
Generated pseudo text using LSTMs (Long Short Term Memory networks) and GPT-2, evaluated how close this machine-generated text is to human-generated text by checking if they follow statistical features followed by human-generated text such as Zipf’s and Heap’s Laws for Words
State-of-the-art text classfication and language modelling in Hindi.
Predicted the price movement of the Dow Jones, Apple, and Microsoft with 60% accuracy by experimenting with more than 100 ensembles of long-short term memory networks (LSTMs). NLP techniques were explored such as sentiment analysis to use as features in the model.
Course Repository for ELL881 (Special Topics:Modern Natural Language Processing), 6th Semester, 2023, IITD
Generation of Simpsons tv scripts using Recurrent neural networks using Tensorflow.
This repo contains all of my submissions to Kaggle competitions or datasets
Built a stock prediction program in which the user can pick any company available on Yahoo! Finances and be able to predict the closing price of the stock based on the set date with high accuracy.
Conditional Latent Autoregressive Recurrent Model for spatiotemporal learning
Sentimental Analysis of Movie Reviews Using Pytorch
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