lstms

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

Layer-wise Relevance Propagation (LRP) for LSTMs

Implementation of the paper Recurrent Independent Mechanisms (https://arxiv.org/pdf/1909.10893.pdf)

Predict Vehicle collision moments before it happens in Carla!. CNN and LSTM hybrid architecture is used to understand a series of images.

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.

Stock Market Prediction & Trading Bot using AI

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.

These are the notebook assignments from the deeplearning.ai Tensorflow course on coursera.

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.

This repo contains all of my submissions to Kaggle competitions or datasets

State-of-the-art text classfication and language modelling in Hindi.

Tensorflow sample codes implementing GANs, LSTMs, convnets, etc. for speech coding

Generation of tv scripts using rnn's of simpsons tv show.

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.

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

Using LSTMs to crack ciphers such as caesar cipher, vigenere cipher and enigma

masters thesis on deep learning methods for speech enhancement

Emotions dataset for NLP classification tasks.

Sentimental Analysis of Movie Reviews Using Pytorch

Music Prediction Project for the CS 6355 Structured Prediction Spring 2021 class at the University of Utah

The idea is to develop a machine learning program to identify when an article might be fake news.

Machine Translation using LSTMs

Naive Image Captioning

Predicting missing metadata with recurrent neural network (RNNs) based entity extraction