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.

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

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

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.

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.

Stock Market Prediction & Trading Bot using AI

Text Extraction with POS Tagging and Deep Learning(LSTMs)

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

Emotions dataset for NLP classification tasks.

Sentimental Analysis of Movie Reviews Using Pytorch

masters thesis on deep learning methods for speech enhancement

Conditional Latent Autoregressive Recurrent Model for spatiotemporal learning

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

This repository contains the work done as part of my B.Tech Project

Uses deep learning to translate Indian Sign Language in real-time

Conditional Latent Autoregressive Recurrent Model for spatiotemporal learning

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