yash-td

yash-td

Config files for my GitHub profile.

Aconex-Notes

Uploading my persoonal obsidian notes for some examinations

wasm-ref

A web assembly file reference for a custom power bi dashboard

Leet-Code-Solutions

Adding my solutions for LeetCode problems while I try to become a better programmer and prepare for job interviews

Badges4-README.md-Profile

:octocat: Improve your README.md profile with these amazing badges.

Data-Mining

Data Mining techniques demonstrates as assignments during my Masters at the Queen Mary University of London

personality-prediction

Predicting personality and moral values of people using music listening history of people.

Image-Segmentation-Seep-Detection-using-UNET

ivy

The Unified Machine Learning Framework

Ethereum-Analysis

Demonstrated the use of Map-Reduce and PySpark to compute average transactions per month, top smart contracts and top active miners using Ethereum's transaction and block data from Google BigQuery. Further, performed scam analysis and fork analysis to find the most lucrative type of scam and change in price after the fork.

Download-Previews-and-Extract-Features

Code used for downloading song previews from spotify and extracting audio features from the same.

Dataset_Creation

Creating a dataset for MSc Project

Creating-an-End-To-End-Dialogue-System

Dialogue-Act-Tagging

Here we'll look at two different DA classification models. The Switchboard Dialog Act Corpus is being used for training.

Coreference-Resolution

Building a coreference system based on the mention-ranking algorithm proposed by Lee et al (2017)

Named-Entity-Resolver-

Training a Named Entity Resolver using bidirectional GRU and Multi-layer FFNN

Aspect-Based-Sentiment-Analysis-with-BERT

Using a pre-trained BERT (Bidirectional Embedding Representations from Transformers) model as a trainable keras layer

Neural-Machine-Translation

A neural machine translation model based on the sequence-to-sequence (seq2seq) models proposed by Sutskever et al., 2014 and Cho et al., 2014. The seq2seq model is widely used in machine translation systems such as Google’s neural machine translation system (GNMT) (Wu et al., 2016).

Aspect-Based-Sentiment-Analysis-

Given a review and an aspect, we classify the sentiment conveyed towards that aspect on a three-point scale: POSITIVE, NEUTRAL, and NEGATIVE.

Comparing-Text-Classification-Models-

Evaluating the performance of various text classification models and experimenting with various types of word embeddings and neural Networks layers.

Text-Classification-using-LSTM

Building an LSTM model to classify movie reviews as either positive or negative

Skip-gram-Model-for-Word2Vec

Training a skip-gram neural network model to obtain word embeddings.

Fashion-MNIST-Classification-using-Deep-Learning

Creating a Deep Learning model which classifies images from the Fashion MNIST Dataset to 10 classes.

Modalysis

Cloud Computing Mini Project

Audio-Classification---Classifying-Audio-Files-into-8-songs-from-MLEnd-Hums-and-Whistles-Dataset

Classifying the hums and whistles from MLEnd Hums and Whistles dataset into 8 different songs. Formulated 2 solutions (basic and advanced). Basic solution involves binary classification while the advanced solution involves more complex techniques of feature extraction and multi-class classification.

Vector-Space-Semantics-for-Similary-between-Eastenders-Characters

Creating a vector representation of a document containing lines spoken by a character in the Eastenders script data. Further computing cosine similarity and improving the same using pre-processinf techniques and adding dialogue context.

CRF-Tagging-in-Movie-Queries

Classifying sequences from the movie queries dataset into 23 different CRF tags

Speech-Emotion-Recognition

Recognises Emotion

stylised_cv

cv

My personal CV Website