Includes examples of code I have written both independently and collaboratively.
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Updated
May 28, 2024 - Jupyter Notebook
Includes examples of code I have written both independently and collaboratively.
Generation of Simpsons tv scripts using Recurrent neural networks using Tensorflow.
Conditional Latent Autoregressive Recurrent Model for spatiotemporal learning
Conditional Latent Autoregressive Recurrent Model for spatiotemporal learning
Predict Vehicle collision moments before it happens in Carla!. CNN and LSTM hybrid architecture is used to understand a series of images.
Course Repository for ELL881 (Special Topics:Modern Natural Language Processing), 6th Semester, 2023, IITD
Karaokey is a vocal remover that automatically separates the vocals and instruments. A deep learning model based on LSTMs has been trained to tackle the source separation. The model learns the particularities of music signals through its temporal structure.
Music Prediction Project for the CS 6355 Structured Prediction Spring 2021 class at the University of Utah
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
Predicting missing metadata with recurrent neural network (RNNs) based entity extraction
Uses deep learning to translate Indian Sign Language in real-time
The course is contained knowledge that are useful to work on deep learning as an engineer. Simple neural networks & training, CNN, Autoencoders and feature extraction, Transfer learning, RNN, LSTM, NLP, Data augmentation, GANs, Hyperparameter tuning, Model deployment and serving are included in the course.
This repo contains all of my submissions to Kaggle competitions or datasets
Text Extraction with POS Tagging and Deep Learning(LSTMs)
Implementation of the paper Recurrent Independent Mechanisms (https://arxiv.org/pdf/1909.10893.pdf)
These are the notebook assignments from the deeplearning.ai Tensorflow course on coursera.
The idea is to develop a machine learning program to identify when an article might be fake news.
Emotions dataset for NLP classification tasks.
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